Generative AI Career Guide 2026 | 60 Questions And Roadmap to Become Gen AI Engineer | Simplilearn

Hi Phil, thank you for joining us from the US. Good morning to you. Hi. I think okay. Hey, we are live on our social platforms as well. Um and there will be viewers there. So uh thank you everyone from for joining us from all over the world. Um I'm thrilled to see so many of you here uh joining us from London, from India, from um USA, from Canada. Okay, couple of you I can see from um Nigeria, China, Philippines. Great. Okay, Pakistan as well. Amazing. So you know this this basically just um tells how uh universal and global this conversation is. Um so very uh glad to be here and glad to have you all here. Uh let me quickly introduce myself. My name is Ana and I will be hosting the session on behalf of simply learn and today's session is a little different from a typical webinar because this is essentially your session. We have crowdsourced hundreds of questions uh from professionals and students just like you and we have handpicked the 60 most impactful ones um to address here today. So there are no like you know slides full of theory in this webinar. Um it's all about just real problems, real questions, real answers and real career guidance. And leading us through all of this is someone who has a truly prol prolific profile in AI. Uh but first uh let me set some quick ground rules. Um right so number one please drop any questions you have in the Q&A box and not in the chat box. Uh we keep getting a lot of messages in the chat box so we might end up missing yours. Um you use the Q&A box effectively we will try to um answer your questions but uh because the the session is also built on a lot of questions already. Uh we will see how best we can incorporate uh your questions as well. Uh secondly um avoid sharing any external links or personal details in the chat box and keep conversations only relevant to the topic. And finally for those who participate in the session till the end and give us your full name the poll that we'll be launching at the end of the webinar we will provide an attendance certificate that you can share on your social media at your resume and even update on uh LinkedIn. So yes allow me to quickly introduce uh Zikarn and brief about what we do for those of you who don't know. uh we provide certifications in career learning paths in various categories like data science um AI and ML geni project management agile and scrum uh business analytics and a lot more. Uh we have helped um 8 million plus people in the career growth across 150 plus countries with more than 50 partnerships. And speaking of partners, we're um you know incredibly proud of the ecosystem we've built built from IITMs and globally renowned institutions to tech giants like Google, Microsoft, IBM. Um these partnerships mean that our learners get curriculum that's not just current but also co-created with the people who are you know building the future of AI. So that credibility flows directly into today's conversation as well. And yes, the proof is pretty much in the outcomes. an 80% graduation uh rate and a 4.8 out of five learner rating. Our programs are built on four pillars which is which are uh cutting edge curriculum thought live by real practitioners grounded in real world projects uh with roundthe-clock learner support. Great. Um so now let me introduce uh the expert who is going to make the next 60 minutes incredibly valuable to all of you. Uh our guest today is Warren Wes. He brings more than 15 years in senior strategy roles uh including VP of strategy and 20 plus years as a hands-on technologist and developer. He's not just but he's not just an AI theorist. He's someone who has built and deployed AI systems and guided organizations through major digital transformation. Um Warren, a very very warm welcome to you. It's a pleasure to host you today. Please do add on to that introduction and say hi to our participants and maybe what they can expect from this webinar as well. Yeah, thank you so much. I really appreciate the intro. U first just a housekeeping thing. I'm not my slide show is not showing up on I don't know which one you're sharing. Maybe I'm missing it, but I'm still on the original screen. Uh from what I'm seeing, I'm not sure what everyone else is seeing. Uh but let me introduce myself. Uh yeah, thank you again for the warm welcome. Um we're going to really get into what people are expecting from from Genai career roles. Uh lots of questions here. We're going to do our best to answer all of them. I think there's some exciting stuff here to cover. So, uh, looking forward to getting into the the Q&A. Okay, just give me a moment. You, uh, thank you for pointing that out about those, uh, slide. I will try to share it again. Okay, I think it should be working now. Yep, I can see that. Thank you. Great. Okay, so um let me just uh set a very very quick context. Um so the webinar, so we didn't want this to be another generic AI webinar where someone uh talks at you for an hour. We so we went out and asked what do you actually want to know? Uh and the response was quite overwhelming. Over 300 questions came in from professionals across 15 plus countries and we read every single one of them. We shortlisted 60 that represent the most uh pressing, most relatable, most impactful career questions across experience levels across industries and geographies. And we have Warren who's going to answer them all. So without further ado, let me just quickly uh kick off uh with the first segment which is all about fitment and getting started. Um this is where most of the anxiety lives right like irrespective of the domain and the industry. So questions like doubts like am I am I the right person for this role? Do I have the right background? Um I have to say these were some of the most heartfelt questions we received. So let me bring a few of them to um live right now. We heard from Karthikan from India who is learning Python and SQL and wants to know what to focus on. U Mohammad from India asked how to get into Janai engineering without a computer science background. Basala from Egypt wants to know where to start if starting from scratch. uh Sahil who's a first semester computer science student from Pakistan is trying to figure out what to prioritize and Sonobar from India again has no AI experience at all and wants to know where to begin. Um so what in the common thread here is um how to get started especially without a technical background. Yeah, let me dive in here. Uh and I apologize I'm still not seeing your screen updating. Okay. Oh, but I I I will get into um the questions and answers while while you're looking at the screens because I don't want to slow you down. Um so I I think this is a a really great set of questions and and they kind of go to the heart of a lot of the conversations I hear from people. Um you know, which is how do I really think about this and how do I think about the technical background and all the knowledge I might need? Um and and first of all I I like how this answer is framed, right? It's it's a domain. This is not a degree. You don't you can get a degree in generative AI. Um but the truth is this is bigger than that. And one of the ways I like to think about this um is is think about AI like electricity, right? It's fundamental. It's it's built into the fabric of our society. It is not just a single skill set. So let's let's go through this slide a little bit. um technical background helps but it's it's not the entry um and I would say focus on building some of that basic fundamental knowledge right prompt engineering rag systems I'll talk about that a little bit more on another slide yet um building applications with LLMs large language models um you can do all of these things without an advanced degree there are plenty of resources out there that will help you get some some fluency around Python on uh and really understand how these tools come together, how to use the APIs that are out there. Um, and there's lots of of partners and resources that you can go rely on. Look at some of the key ones. OpenAI has some great resources to help you understand these things. Uh, the same with Google and Gemini and their tool sets. Um, and I would also throw Anthropic and Claude into that set as well. All of them have great resources to help you understand these basic levels. Now I want to think about kind of these this notion of three layers. So think about layer one is is the fundamentals. How do all of these things work? Um what are transformers? What is rag retrieval? Augmented generation uh for what it's worth. And like I said, we're going to cover a little bit more in some in some other questions and answers. Um what is fine-tuning? So these these are the basics. Uh and you will find resources for these everywhere, including some we'll cover a little bit later on. Um, and then get hands-on. Um, I mentioned OpenAI, I mentioned Anthropic. I would also throw Google in there and others. Think about getting hands-on with the tools that they offer. Um, they all are moving really fast. They all offer a ton of great educational components that will let you dive into these, use them, leverage them. Um, and usually they're free. Uh, also think about some of the tool sets that apply on top of this. So, Lang Chain is is currently a really popular one. Um, HuggingFace offers a lot of different tools that you can get into. And all of these are really going to help you understand what's available and how you can move forward. And then layer three, applied projects. And and I think this one's critical and this is going to be a theme that you're going to see. Create real projects. These need to be part of your experience and they need to ultimately be part of your portfolio. show that you're actually making progress and you're solving real world problems. So, thinking about this from a six-month action plan. Um, learn, build, publish, repeat. I love how that looks. Think about getting your fundamental knowledge in place, right? Learn some Python, learn how to integrate with the APIs that are available to us through all these great providers. Um, and then move through that and get some comfort. Then build a working app. Well, this should be something you're familiar with, a topic that is important to you, but it's really critical to build something that works and leverages all those great tools that are behind the scenes and available to you. And then talk about it, publish it on GitHub, write on LinkedIn. There's a lot of sharing on YouTube and X that can really help you see uh what everyone else is doing and show off your work as well. Hopefully that answers some of the questions related to that topic. Yes, thank you. Moving on. Okay, there seems to be an issue with Hey, just give me a second. I'm so sorry about this. Okay. So um yes moving deeper into you know fitment. Let me just quickly read out the questions. Anjel from India is an MBA in HR um who caught in the exhausting experience paradox. Can't get a job without experience. Can't get experience without a job. Uh so that's the dilemma uh she's in. Purya who's a BCOM graduate from India wants to know the single most important thing that she can do right now to get started. Uh Sanai from India again is in pharmacy and wondering how AI intersects with this future. Steve from Canada is a high school graduate who has learned Python and is asking what's the realistic path after that and uh Titio from Nigeria is being told to specialize but doesn't know the landscape well enough to pick a lane. So, uh, Warren, how do I think the common thread here is how do people from nontraditional backgrounds find their entry point into Genai? Yeah, the these are great questions. Um, and I think you're going to see a theme here as as we start to answer these. Um, so love the first bullet point. Stop waiting for the experience, right? You don't need to go start trying to find, you know, a a diploma that's going to get you there. use educational resources, but the most important thing is get out there and start building it. And I think the most important thing that I see here is start building something that applies to your domain. Bring your expertise to the table. I want to share a fun story that I just saw recently. Um, Anthropic, who is responsible for cloud code and a lot of other amazing tools, had a hackathon just a few weeks ago. The winner legal person, it was a lawyer, wasn't a coder. And that was because he brought in his domain experience. He built a genai project related to his expertise and that's where the success comes in. And I think this is going to be critically important moving forward is bring your experience rather than kind of the notion of tech. I think experience is what's going to win. So just reminding this that domain knowledge plus your ability to really leverage AI, I think is where the strength is really going to come. That's not to say that there aren't roles that place specifically in the technical space, but I think this is where we're going to see a lot of traction. Use the expertise in your domain. Example here is an HR professional who understands AI for talent acquisition. You know, expand that to any domain role. Um whether you're in HR, you're in sales, you're in marketing, you're in engineering, all of these roles can benefit from bringing AI to the table. So, start thinking about going a little broad first, right? Think about understanding how all these tools work and and getting the fundamentals in. Like we said in the previous slides, learn how to use them and then really think about how you can use them to solve a problem in your domain. Something that you're close to that you already know probably how to answer. AI can be a tool for you, not just an an overall hammer. Okay. Okay. then I will move on to the next segment. Um so section one was about getting in um you know getting into the domain. Section two is for those of you who are already in already working already contributing um but feeling like the AI wave is accelerating around you and you're not sure how to grow along with it. So some very real questions came up. Um let me quickly read them out. uh Manish uh who's a principal software engineer with 14 years of experience from India wants to leverage AI to move into an architect role. Um Ajinda who's a data analyst with four years of Python and SQL experience wants to transition into a genai role. Aisha from Nepal is a SAS product manager whose team uses AI daily but doesn't know how to lead AI initiative strategically. Um, Shri from Ghana works in finance operations and feels left behind as AI co-pilots get uh get introduced and Sally from the Philippines um so virtual assistant specializing in CRM and marketing automation wants to move into AIdriven roles. Um so Warren for people who are already established in their careers what is the upgrade path into Genai look like? How can they keep themselves um you know more in date? Yeah, this is a really exciting place to be in because you're you're in a spot where you can already leverage your experience and your expertise to scale into this new AI world. Uh so let's go through some of the different roles and and think about how those apply. Um so thinking about um engineers, thinking about moving to architect, master these core AI design patterns and and what I like about this list here. So, rag architectures, I talked about that earlier. Retrieval, augmented generation, very common use case. It's used all over the place. Um, agentic design patterns. This is kind of the new world. Everyone's thinking about agents and how we use them. And then there's LLM ops, right? How do we manage and and run this entire thing? Because there's a lot of things to think about. And what I really want to talk about here and focus on is is don't think about the super new exciting things that are all coming down the road. You know, rag architectures is still one of the most common use cases and some people might not think it's the newest exciting thing, but there's a huge huge need for this. And if you can master that architecture, you can really drive value uh for the people that you're going to be integrating with. Uh data analysts, I'm always a little envious of you. you know how the data works, you know, and this feeds so much into the fundamentals of LLMs and and how all this works. Use that knowledge. Um, it can help you figure out how to build these pipelines and how to bring all that data into these systems. Um, VAS, think about workflows, how will you use these tools to better plan out how all of these things map together. Uh, and then I want to talk about prompt engineering here just really quickly. I think a lot of people still feel well maybe prompt engineering is going away. It's not as much a skill. That is not true at all. In fact, I believe it's more important than ever to make sure that you understand prompt engineering to really drive these solutions. And then finally, when I think about product managers, it's really important to apply judgment. Uh this is still a human skill, right? Does it satisfy a need? So is a solution really delivering what we want it to deliver and is it meeting the needs of us or our clients or the people that we're bringing these solutions here for? Judgment is going to be key to how we interact as humans with these systems moving forward. Great. So now we move to the question of leadership and differentiation. Um, Franuis from Cameroon is a project manager with 21 years of experience and PhD and he wants to know how to position for a director level role in the AI era. Um, Ali from Pakistan wants to know how to market and monetize AIdriven product. Uh, Pierre from Dr. Congo is a PhD researcher wondering how Jai can make his process more efficient. Uh, Himmani from India is a digital marketing executive from the '90s generation who is finding it hard to keep up. and Rajiv from Singapore is frustrated that everyone around him is calling themselves AIS uh after just like you know a few GPT sessions. So how do you separate real depth from surface level usage? So Warren what truly separates um AI professionals who rise um from those you know who differentiate how can they differentiate themselves from those who just know about AI? Yeah, that's great. I really love that AI savvy content. Um, you know, I'm honestly I'm seeing that a lot. You know, people get a little bit into AI and and they call themselves an expert, but there's a way to separate this and really show that you bring some knowledge. So, let's talk about the leadership part first. Um, and I think this one's really important and and I was just talking to a colleague about this the other day. Um, which is we need to be able to do strong storytelling. Talk about the solutions that AI can bring to the table. And not just a cool demo. Talk about the cost. Talk about how much you drove revenue. You know, these are real things that your leadership teams are going to be looking for. And the leaders who can really tell that story and talk about those key metrics are the ones that are going to separate themselves. Again, it's really easy to go show a cool demo because they come together so fast. You need to think about the business case and the true metrics underlying that everyone's going to be asking us for. Uh, we want to sell AI based on outcomes, not based on tech. You know, I I've heard so many times, hey, go put this new cool tool in that's using AI to do something. That's not really what's going to drive business value for the people that you're talking to. Talk about the outcomes. You reduced contract review time. We increased revenue. We saved a whole bunch of time by being able to automate these specific workflows. It's really important to think about outcomes first and not the tech or the bells and whistles that are really important. And then finally on this bottom one, I I've been working on some projects this week that really come to life in this bottom one. Um work through it and fail, right? Find out what's not working. I can tell you I I just blew one of my cost budgets literally just yesterday. Too many tokens went out the window and and we had to start from scratch. And now it means we need to go back and figure out what that solution did, why it didn't work the way it's supposed to work. You need to iterate through these things and really see how projects work in the real world when you're trying to struggle with those things and showing what's going on. This is where you're going to show the difference. Show your successes. Show your failures as well and show what you've learned along the way. Great. Thank you. So moving on to the next segment. Um now I think we're getting into what um could be one of the most emotionally charged uh sections which is career transition. Um these are the questions um from people who are coming from completely different fields and wondering whether the door is still open for them um in Genai. So let's hear those stories and oh if um you know you see your question here and you're in the audiences please do like you know uh let us know like you can um maybe drop an emoji. We'd love to know if you're here and you know um address this uh question to you specifically. Right. Okay. So um yes, let me quickly read the questions. Uh Stephen from Nigeria has 10 years in logistics and wants to transition into Genai. Um Nathan from India has 10 years in back office operations. Uh Shinani from India is an electrical engineer wondering where AI fits in her journey. Ali from Yemen is an English language instructor wanting to get into edtech with AI and Peter from Macedonia is an embedded developer wondering how the transition process works. Um so Warren is um a full and complete pivot into Genai actually necessary you know is there a smarter path for people coming outside tech? Yeah that that's a great question and and that's a really good way to to position and think about it. Um, you don't need to jump feet first into a full AI role. Um, and and I really like this notion of of this adjacent move. And we we hit upon this a little bit earlier. You don't need to abandon your domain. What you're an expert in today. As a matter of fact, I think this is where we're going to see a lot of the real benefits from rules take place. Find an AI use case within your industry today. Find a problem that can be solved with AI and apply it today based on what you know. You know this example, logistics experts who understand AI powered route optimization are going to are in massive demand. This is happening everywhere. People are figuring out how to use AI in their roles and those are the ones that are really starting to show real value. Uh example here, educators, I think this is a good one too. I've I've seen some of the courseware related to how instructors can better leverage AI to to do better in their roles. You understand as an educator how to bring this information to your students, how to position all this this information so that they can learn better. How do you use AI to make that better? How do you use AI to really bring value to that particular area? And I think this is where we're going to see the best solutions, right? You understand the content, you understand the subject matter, you're going to be the one who can leverage AI to bring a solution forward that's going to make this more effective. So, think about it from this three-step transition. Think about how to apply these AI tools to what you do today. And then this is important is define and document the impact. What happened? What did you see? What were the improvements? And what were the gains that you realized? translate that into something that you can now put on your resume and now put in your LinkedIn profile so you can share with people the success that you had and how it worked and how you were able to move the needle and then start to build a project that shows how you can bridge your domain expertise into a generative AI solution. You're going to hear us talk a lot about portfolio and really showing your project chops. This is an example of that. And I think being able to tie together your domain expertise first and foremost with how you can bring an AI solution to market is really where you're going to see the strength. Great. And what about more um senior professionals like those with decades of experience wondering if they're um you know if they've left it to too late. Um so here are some questions on that line. Sep from India is a solution architect with 20 plus years of experience wanting to transition into an AI architect role. Uh Robert from Canada is a 45 um is 45 years old in financial consulting and is worried about age bias in AI spaces. Ash from India is a principal aerospace engineer and wants to implement AI as a project manager. Josha from India is a senior software developer already learning LLM and agentic workflows and Melanie from the USA's project manager who has um done some JI consulting and she wants to go full-time in AI operations. Um so Warren is seniority an asset or a liability when moving into JI geni? Yeah, that's a fantastic question and you know I wanted to touch on on Robert's question specifically. 45 years old. Everyone in the AI space seems younger. Um I I can tell you I'm I'm a little older than 45 years old and and I feel I'm having great success uh in the IA space. And and I think this is because seniority and AI is a good thing. we can bring our knowledge especially in spaces that maybe people younger than us or junior don't really know yet and we can really help um illustrate how those things fit when we're thinking about the AI solutions. Um and I and I know there's a lot of discussion especially in the software space you know are we making software engineers obsolete. I I don't believe so. I just think we're making them more valuable especially the senior ones who can really bring in enterprise solutions and systems thinking experience. You know these are skills that are not going to go away. Certainly not tomorrow. you know, maybe way down the road something changes with AI, but in the very near term and and probably for a while, we need a lot of this experience to help drive these solutions and make them stronger. So, I really feel strongly that seniority in this space is going to be critical. Um, for senior development or sorry, for senior developers, think about LLM engineering as an extension. you already have the strength, you already have the core skill sets that help you build really strong solutions. Now bring in LLM engineering to that space to see how you can use AI to drive new solutions. And I think you're going to see some really strong benefits from being able to do that. I don't really feel age bias is is a negative. The seniority here, I believe, is is really strong. And this comes back to one thing that we talked about earlier too, which is trust. You know, a senior person in any of these roles can help establish that trust for the people who are putting their trust in in people to deliver solutions. Um, and that's going to be really important because one thing I hear is anyone can go get a solution from chat GBT or or another chatbot today. But it's the people with the experience that can pair that that AI value with really strong experience to say this is what a holistic solution looks like and this is the best way to deliver it. So I I believe seniority actually brings a premium in a lot of these spaces. Great. Uh so let's talk about something uh everything you know everyone thinks about but few people ask out loud uh which is money and advancement. So this section is about understanding your worth in the AI economy and how to claim it. Let me quickly read out the questions. Okay. Rohit from India is a fresher um getting offered about um you know four to five lakhs peranom but feels he's worth more and wants to negotiate. Scott from the USA has 20 plus years in stock trading and wants to pivot into AI crypto at a $80,000 salary. Is that realistic? Um, Obina from Nigeria is a genai freelancer being priced against local rates with while serving US clients. Um, Zara from UAE wants to understand the actual global salary landscape across GI roles and Priya from India is doing measurable AI work but her company hasn't adjusted compensation yet. So, um, Warren, how do AI professionals negotiate their true market value? Yeah, this this is a tough one. This is a fastmoving space, right? There's there's so much happening and there's so many new rules that are coming up. Um, so you're you're going to want to continue to do some research in here, which I'm going to touch on shortly to make sure you're aware of all the things that are happening here. Um, so I want to give the team a ton of credit. Um, we've got some global salary benchmarks in here that are going to give some indication. Now, I would say, like I said, keep an eye on what's happening here. This is a really fastm moving environment. Um, and you're going to want to see how these roles are not just shifting, but how their salaries are shifting as well. Let's run through these pretty quickly to give you a sense of what you might see. So, for generative AI engineers, uh, in the US, you could see a range from 120K to 250K. uh in the UK you're going to see that from 70,000 to 150,000 uh for India 18 to 40 to 40 uh AI project managers kind of a similar range right uh in the US 130 to 180k and then AI strategist and consultants this is kind of a big range because they really differ in what they might deliver and the experience they bring to the table but you can see a range here of 150 to 300,000 so there's some decent salaries out here that you can chase if you can bring this AI experience to the table. Um, and it's not just these specific AI roles. You can see in areas like finance and healthcare where the pay is higher when you're bringing AI experience to the table. One thing to note, and this is a critical one, market research is your first negotiation tool. Use the AI tools to help you do this. Use chatb, use cloud deep research. All of these will help you figure out what's happening in the marketplace today and you can go be better informed before you have a conversation. I think I'm getting a little bit of an echo there. I'll keep going though. Um, if if this is new for you and and you're a fresher or you're thinking about a career change, this is where that project portfolio is going to be so important to you. You want to show real working products. You want to be able to demonstrate that you get it, you understand it, and you built something that's real. And that's going to be a critical factor that separates you from other folks who say, "I know AI. It's okay." Show that you understand it. Show that you can really make a difference with a project that works. I'll also say this is a skill, right? You want to come into this prepared. This is where you can use these tools. Use chatbt to better understand how to negotiate your salary, what to discuss, what kind of information you want to bring in. Don't be scared to ask these tools for help to try and figure out the best way. I like this one on the bottom for freelancers is is pricing on value. And this relates right back to what we talked about with with real work. This isn't about a tool. This is about the value you bring. This is about the business metric, right? I automate lead qualification. I I help negotiate and manage workflows to make things more efficient. So price on value, don't just price on hours or don't just price on the fact that you've got a tech tool in place. I think this is going to be really important as you try to negotiate how you bring AI experience to the table. Perfect. Moving on, let's talk about promotion and career longevity. May from China has been using AI to do the work of three people but has not discussed that with her manager. So should she? Uh Claudia from Germany wants to build career capital that won't become obsolete in two years with you know so much changing um every year. Namin from India is a senior director of operations wanting to anticipate disruption before it hits. Uh Tariq from Saudi Arabia wants a management track um not just an individual contributor path and Dr. Kashi from Bangladesh is a data scientist looking to grow in international markets. Um so Warren, how is it possible to use AI as a lever for promotion or career growth? Yeah, these are really good questions and pretty common ones and and I and I particularly like transparency. I wanted to come back to to the question on using AI tools to do the work of two to three people on my team but my manager doesn't know. I I read a great article recently that that called generative AI a time vampire and this was the example they used which was people figuring out how to use AI to actually be far more productive but they're not sharing it. Well, the risk you're going to see there is you know 3 months from now you've been moving at this crazy pace and now you've set the expectation. That's just the norm. The better way to think about this is show the value you're using of applying AI to your role and how it's making you more productive. That's going to open up a couple different things, right? One is going to show that you are smart enough to figure out how to leverage AI and you've used the skill sets that you've built in AI to be a more productive person, to be a more productive employee. But maybe more importantly, especially if you're looking to shift into a more AI focused role, you've shown how you can use AI to do that. That means you can apply those skills in different parts of the organization. So, if you've you've increased what you've been able to do in the sales organization, for example, using AI, well, why can't you bring that over to the finance organization or the operations teams? All those skills can now be translated to those other areas. So, don't be shy. show that you're actually good at this and that you can leverage this across the organization. I think it's going to be increasingly important for leaders to show that they're fluent in AI as well. This isn't just about everyone doing the work behind the scenes. We need to understand what AI means across the organization. And especially as it grows and especially as people are still trying to figure out how all this fits, our leaders really need to understand how to support those conversations. And some of that can be helping them understand what the roadmap might look like or just giving them the right resources to make sure that your teams are ready to dive in and jump into this critical skill set that's going to be important to all of us. Um, and I think this is important down here is is things change, right? Tools change, model changes. What doesn't change is the skills that we bring as as humans, right? finding problems, solving them, framing them, telling stories we talked about earlier. These are things that we we have as humans. These are our capabilities that AI isn't going to easily replace. So, think about those and how they fit. And then layer in the tools and the models and all these other things as well because those are things that going to support you as an expert. And this is where, you know, we continue to talk about domain, experience, and expertise. Those all fit together. We are now on to the fifth section. Um, and this is one of the most practical sections of the day. How do you actually learn this stuff given everything else going on in your life, right? Um, the overwhelm is real. So, let's uh address it directly. Okay. GI from Pakistan wants to know how to filter what's actually worth learning versus what's just hype. Um, Tarun from India is a data analytics beginner wanting a road map to be job ready in 3 to 6 months. Uh, Ria from India is already working full-time on AI chatbots and wants to prepare for her next role. Um, Ian from the USA is over by seminars that are ultimately just sales pitches and wants a genuinely cost effective learning path. And Maria from the Philippines has a full-time job and a family and can only commit about 5 hours a week. So, how uh does how can she make that count? Um so, Warren uh I think I'm going to like, you know, pick a common thread here and and um post this one question. How do you cut through the noise and understand um what what it what actually matters to be learned? And especially if you have a specific timeline or a goal to achieve, how do you filter what to learn? Yeah, the the the noise is definitely out there, right? We are being bombarded with changes and happenings in the AI world. Uh and it's really hard to keep up with all of this. Um but I think what you want to do is is come back to first the fundamentals. You know, think about the things that you're always going to be here and and like it says in the notes, you're slow to expire. So how do LLMs work at the basic level? You don't need to be a machine learning engineer, but you should know the fundamentals of how these things come together. Those are core skill sets that are not going to go away. Think about things like rag architecture again, retrieval, augmented generation, prompt engineering principles. I really feel those are not going anywhere fast. So think about the core, think about how you need to get that knowledge in place and that's going to set you up for success moving forward. Uh and then think about some of the infrastructure things that are important. Think about Python, um, OpenAI, Anthropic, some of the other tools that are pretty common that that are going to be fundamental to the a lot of the work that you might do on a day-to-day basis. I would say really focus on thinking about how to integrate with the OpenAI APIs. We mentioned Anthropic, there's there's others, right? Google, Meta, there's all these API layers out there. For the most part, they're all the same. If you start with the open AI ones, you're really going to get a common grounding on how all these things come together. Now, what you want to think about and evaluate and maybe learn about, but just be aware of is specific models, right? GPT 5.4 just came out. What does it do? How do all the features fit? That's great. It's good knowledge to have. It's not as important as having those fundamentals. So, you don't want to get lost in trying to keep up with every last model release, everything that's going on. You really want to focus on the core and the things that are going to affect the tools and the productivity that you bring to yourself on a day-to-day basis. I like how this is framed as a hype test. Does it solve a problem for you today? You know, you are literally going to see a new tool launched this afternoon. I can guarantee it. But it might not help you. it might not apply to something that you're working on today. So, think about that first before you reach out to that shiny new bell and whistle. Does it actually help you solve a problem? If it doesn't, maybe leave it for later cuz something will come up that's going to help you solve a problem that you're facing today. And then I think about structured time. You know, put the time aside to help you get through this and to help you build your knowledge. So this is a little bit of a a timeline that you can think about in week one. Look at a concept and really dive deep into it. So whether that's on X, whether that's on YouTube, whether it's any of these other educational resources that you can get your hands on, get really deep into it. Then in week two, apply it to a project. Do something in the real world with this. Really, really write some code. Really build out a solution so that you understand how it works. that and I can't stress how critical that is. We learn best through hands-on and we learn best through realw world projects. And then week three, write about it. Tell people, share what you've been doing. That's going to help you not just think about how to communicate to others is going to build your portfolio and that's going to help your career transition move forward as well. And then in week four, look at all the things that you've done. capture the lessons that you learned and then think about what you're going to build next and iterate this. I I've seen a lot of creators online talk about this notion of launching something new every four weeks. Kind of fascinating, kind of scary at the same time, but all these tools actually make it possible to do something like that. So, challenge yourself to continue to dive in and continue to go through this process. Brilliant. That's a great piece of advice. I think applicable to everyone. Thank you. Um okay, moving on. Um let's talk about upskilling a bit more. So Jay Kumar from India has 10 years in supply chain and logistics and want to like you know get more keep himself updated on ji in order to stay relevant. Um someone from South Africa has a strong foundation in data and Python but not a computer science degree. um how can they best position themselves for a geni role. Amal from Jordan is a supply chain and procurement specialist wants to integrate AI into her existing work. Uh Pra Pravin from India's third year BTE CSE student targeting internships in AI and ML and Arjun from India is already working in AI feels like he needs to read three research papers a week just to keep up. So uh quite a weight where do the real um you know career opportunities live in uh domain specific AI. So how do you um stay current without burning out? Yeah that yeah these are great and and you know you're going to hear this repeated theme domain specific is is going to be the key for most people I believe. Um, and this this top bullet point I this is just one of the most important takeaways I think that you can come out of this with is is a project beats a concept every time. Show real world projects. Every time you talk to someone about potentially taking a role on an AI, they're going to ask you to prove it. And they're not going to ask you to prove it with just tell me some some answers to some key questions. They want to see how you have done this in the real world. These projects are crucial to be sure that you can be successful in this space. Thinking about you know supply chain and operations and all these other roles to be honest your domain skill is a premium. This is where you bring the value. Think about how you can use AI to solve a problem in your space. And and one thing I want to stress here is don't think about the most exciting or cool or huge problem that you can solve with AI. Think about the boring ones, the tedious ones that happen day in and day out. But AI can help you solve that. Those are the ones that honestly drive the real value. And when you see AI consultants and engineers out there having real success, that's probably where they're focusing on. Now, you can stay current without getting drowned. And I would focus on a few that you trust. Uh, you know, five to eight high signal s uh sources. And these can these are some examples but these can be any that you find that you're getting value from because they may be specific to your domain. Um or they may be just technical in general. So Anthropics got a great great research blog. Um OpenAI has got some great articles and learning resources that can help you keep up to date. Lang Chain's got a good change log. Um and the batch by deep learning.ai is also a good resource. I would say find a a creator on YouTube or X that also hits some of the technical topics or domain topics that are relevant to you and follow them as well. Um, and you don't have to hear from everybody. You don't have to consume all the information that's coming your way. Focus on a few. That information is going to come in and help you with your specific domain or experience. Okay. And now we reach the final and perhaps most strategic section of today which is you know the big picture. What does the GI job market actually look like right now and where is it heading? So these are the questions that will uh help you place your bets wisely. So let's get into them. Right. Amara from Ghana wants to know which genai roles are genuinely in demand in real job postings and which are already getting commodit commoditized. Uh Jared from the USA wants to know what types of companies actually hire geni engineers and how to find them. Gita from India is an SAP MM consultant thinking about switching to um switching. So how can she uh use AI um in her role and upskill um and someone wants to know uh how to understand how AI transforms um their role and Sher from the USA is already using AI for onloading screening but it's making bad decisions. So how uh does she fix it? So uh Warren again like you know I'm going to um these are like you know quite diverse questions so I'm going to pick like the common line here. Um is the geni job market as strong as it is uh as it looks from the outside? Yeah, I my I believe it is as strong as as it as it looks and and I think we hear a lot and part of the the struggle that people see is it there's a still a lot of shifting um and there's still a lot of stuff happening and that's going on but there definitely is a sense of high demand roles. Uh so walking through what some of these are. Um LLM application engineers, right? How do you take and use an AI to help deliver and develop a a good experience? Um rag developers talked about earlier retrieval augmented generation. This has been a common use case that's been around for years and is still really highly in demand. Um product managers is becoming a bigger role too, right? Someone's got to take charge and move all these products forward in the right way. Um, I think what we're going to see too is we talk about MLOps and LLM ops engineers. This is a a really important focus area. A lot of people think about kind of the exciting stuff up front, the development, you know, creating these cool new applications, but these things require a lot of care and feeding behind the scenes. So, how do we make sure that we've got the right roles? They're going to take care of that. That's a huge growing space as well. Um, governance is something that we're hearing a lot about, right? How do we ensure that we've got the proper tools, policies, and procedures in place to make sure that we're using AI in our organizations in the right way? So, lots of really core key roles that are pretty in high demand right now. And I think that's list is just going to grow. We're going to see more that's going to come over time. Um, outside of big tech, you can look across pretty much any industry right now and you're going to see a demand for people that can bring AI skills to the table, especially in this AI engineer and LLM development space. People are looking to create new solutions in their organizations and they need to bring those skill sets in. Um, when you think about SAP and ERP professionals and I think this is actually going to apply to any large large software stack too. um SAP is aggressively embedding Juul into their tools um and they're going to need people that understand AI to help them figure out how to do that. So if you already got the skill sets in those particular areas and you can go learn and figure out how to leverage AI in those spaces that is going to make you a premium. You're going to be able to come to the table and do things that other people can't. Um and it's at the end of the day and it's the same theme that I think you're hearing. It's the mix, right? It's domain knowledge plus AI knowledge. Those things coming together is is the real power. Um, and that's essentially what the the bullet point on the bottom says for for HR professionals specifically. AI can help elevate your function. U, but it still can do a lot of things that are important, right? Build culture, employee relationships. There's a lot that happens that humans still need to bring to the table. But the people that can do those in their in their domain and apply AI in the places that it makes sense to are going to be the ones that really start to advance. Great. Thank you. And finally um you know about the future. So Fatima from Mexico wants to know what Genai job titles will exist in three to five years that don't exist today. Um, Himari from Japan is asking whether AI agents will make geni genai engineers obsolete. Uh, Elina from Ukraine wants to know if this is a real sustained wave or a bubble she should be cautious about. Uh, Batiswar from India is a geopol geospatial data researcher wondering if industry will choose him over a professional developer who just upskilled in GI. And our final question, you can take it. Um um you can take that. This is from the simply learn team. Um after 59 answers today, what is the single most important piece of advice for anyone at any career stage um who wants to build a meaningful geni career? Yeah, I I love these questions. Um I want to talk about the bubble first. Um I I I do not believe that we have a bubble that's about to burst. Th these are meaningful things that have happened in AI that are going to be with us for the long term. You know, I talked about a comparison to electricity. That that's really where we are. Don't think too much about the noise and the press because they're going to bump up and down and they're going to communicate the most dramatic thing that they hear today. But if you just look at the pure uh evolution of things that have happened even in just the past few months, you'll understand that these things are here with us. They're here to stay. and they're only getting better. This stuff is not going away. Um, same theme again, domain specialists are going to win. Uh, some interesting new roles to think about. So, Agentic AI, we're all hearing about this. This is kind of the future and this is critical. It's important. We're going to see a lot more roles pop up in that particular space. Some of the ones that maybe we haven't hit on, governance, right? someone needs to oversee all this especially as we see new regulations and new compliance things come up going to be critical for that uh synthetic data engineer uh these companies that train these large language models they're running out of data there's a huge value for people who can put synthetic data together so when it really comes back to it it's about the domain specialist um you know I I I love the example I brought up earlier had a hackathon the winner a lawyer not a coder. Why? Because the domain knowledge is where the experience came in. So that's where to really leverage your existing skills. Uh I want to talk about the the internet bubble burst in 2000 and and you know a lot of people saw that as as a an interesting moment but you look at where we are today and how the economy is built on that. So even though we saw some companies fail and we saw some some interesting failures in that particular time that underlying infrastructure powers everything we do today it's core and I think AI is going to be even more important to the future uh than the internet was to where we are today the biggest piece of advice and and I've said this a few times but I I think it is the most important learn and build. Building is going to be the most critical thing that you can do. Build a real project, test it, try it, make sure that it's solving a real world problem. It's just not something that looks cool. That is where you're going to get the most value. Continue to do that. The more of those you can build, the more you're going to learn, but they're more you're going to build your portfolio so you can share that with other people that might be willing to give you a chance. Brilliant. Thank you so much Warren. Um and I think with that is the context uh um um you know let me also take this time to introduce uh Michigan engineering professional education's applied gen specialization. But before that Warren uh this was absolutely phenomenal. Thank you. Listening to like you know all of these questions today like one thing is very very clear. Um it's it's not that people lack motivation. It's just that you know sometimes they lack a structured part and that's exactly what I want to uh share with uh everyone before we close. So if today's session like showed you the what and why um our applied generative AI specialization built in collaboration with Michigan um gives you the how. So let me walk you through it very uh quickly. Right. So this is a 16week live hands-on program with 70 plus hours of instruction from industry experts. Um you'll walk away with an elite certificate from the University of Michigan, one of the world's most respected engineering schools. Um the curriculum goes very deep like from RA to fine-tuning to agentic AI it covers a lot. Um and everything is applied through real projects. So this isn't a passive course. It's um um you know it it involves a lot of practical learning. Um right so here's how the learning path flows uh from Python and AI lit basics through advanced AI architecture building LLM applications all the way to agentic AI frameworks with model context and tooling protocols there's a lot that this uh curriculum covers uh plus electives in Microsoft Azure AI and Microsoft copilot um as well uh for those of you asked to warrant today about um you know agentic rank LLM ops this is where you can go deep on all of it. Um Warren like you know if you'd like would love to hear from you here. What is your take on the curriculum? What kind of learning experience um can like you know people expect with simple learn? Yeah. What I like about these curriculums is is they're they're not just going to show you technology and how kind of AI is built. They're going to show you how it gets applied in the real world. Um, and I and I think that's important and I think that's what gets missed in a lot of content out there is is it's not just code. It's not just technology. It's how we bring this into the real world and actually solve real problems. Great. Thank you. And um just like you know uh in brief the skills and tools that will be uh covered as part of the program. So you know from prompt engineering, AI agents, agentic frameworks, lchain transformers, stable diffusion, retrieval um augmented generation, geni governance and a lot more um across like you know industryleading tools like open AI um Azure AI studio, hugging face, lang chain, gemini, info pilot and a lot more. So this is the curriculum you know um designed for the roles uh that we discussed about in the session and like Warren kept insisting about you know building your portfolio working on projects um so projects like you know beat um concepts on every resume. So in this program you'll build seven portfolio grade projects an AI powered business intelligence assistant, an HR chatbot, an AI design studio, a news information assistant, a Python adventure game with GitHub, copilot, interactive storytelling with GPT and customer order analysis. So these are like you know pretty much um the projects that um make you make your resume very uh impressive. And yes, don't uh you don't have to take just our word for it. Here uh is what learners are saying. Micah, who's a founder from Washington, said the hands-on projects helped him turn his vision into a tangible goal. And Lauren, who was a digital marketing supervisor from Detroit, said it exceeded our expectations and boosted a career. Um, so these are working professionals just like you know a lot of you today that we've heard from. Um, and yes, we don't just leave you with a certificate, you get career services too, um, from Simply Learn's job assist. Um this includes AI powered profile optimization for your LinkedIn and rumé interview prep and mock assessments, handpicked job opportunities curated by simply learn and group mentoring and networking with peers in the industry mentors. So this is pretty much the full ecosystem you need to land um the next role. Um right so for those of you who are interested here are the program investment details. uh for Indian learners the fee is 1 lakh49, um999 with monthly installments starting at just $6,716. For US learners, the fee is $2,995 with installments starting at just $300 a month. And for for our audience from other parts of the world, sorry we're not able to give you the exact um program currency. Um let me just share the details on the chat. Um so you can um explore more about the program. Just give me a moment. Yes, the program link has been shared in the chat. So please do explore um the details there. Right. So um with that I'm going to launch a poll to quickly take your interest in enrolling in the program. Um even if you have any queries you would like to understand more know more about the program about the career prospects um then please do uh click on yes uh our expert advisor will be getting in touch with you um and guiding you further. So, um yes, uh I'll have the poll on for um a couple more minutes. Meanwhile, uh Warren, we have like you know we can we can take maybe one more question that I found very interesting that I come come our way. Um so I'm going to read that out and while we have the poll live, we can uh maybe take this question. Um so we have a high school student here who's curious to dive deeper into the code world. uh by the time this person graduates in 2030 um will a traditional engineering degree still be enough or should this person start building a geni portfolio um before the first day of college to stay relevant um should what should they focus studying on and by the time they enter the job market in four years many entry-level coding jobs might even be automated so what is like you know some human skills that they should develop during the college you said Jenny I cannot replace. Yeah, fantastic question. I I saw that in the chat. I'm glad you brought that one up. Um so I don't I don't think coding is going away. Um not completely anyways and I think the core knowledge of coding is is going to be important if you want to get into this space. Um, now that that said, I would probably think about getting into AI first programming and prompt engineering because I think that's going to give you the most bang for your buck right now. Um, and then that will also help you with with what you said about building a generative AI portfolio. You know, we we said that in the Q&A earlier. I I do believe this is critical. Start building some realworld projects that's going to help you in a couple ways, right? is going to help you learn how to use those tools and and it's going to help you learn how those languages integrate as well. Um, and then I would also add, don't be afraid to let AI help you code. This is actually a skill that is coming up that people are really starting to rely on, which is how do you direct AI to code for you and make better projects because of it. Um, that would be a key one. Um, as far as a human only skill, um, you know, I still think there's something about taste and discernment. Being able to look at a solution, being able to look at a project and say, "This works. This actually is going to drive value or benefit for people." Um, so being able to assess that and provide judgment uh on something uh is still going to be something that humans are going to need to do for a long time yet to come in my belief. Amazing. Thank you so much War and I hope uh that answered the question. Great. Okay. So with that I'm going to also end this poll. We have gotten quite a lot of interest in enrolling in the program. Uh right and yes onto the section that lot of you would be eager to know about. I will be launching another poll um in just a second where you can enter your full name. Um uh please ensure that you give us just the name that you want in the certificate and not any additional details like your email id. We already have that. Um so once you um submit your name in the poll, we will be able to generate the certificate and share it with you on your email within the next 48 working hours. Um in case you're not able to access the poll, it should be um accessible only if you're joining in from a computer or a laptop on phones or if you're not using the Zoom app, it might not uh work for you. So in case if it's not visible um or you know you don't get the certificate, please feel free to write to us at webinars@simpslearn. We will um get in touch with you about the certificate. Great. Um so Warren I think like you know I owe you a very very special thanks. Um this was this is a very great session. It was one of our um you know different initiatives and I cannot think of like you know better person to have helped us uh lead drive the session in a very successful way. Um answering 60 questions um in as personalized a way as possible. Uh it was it was truly uh truly you know uh delightful. Thank you so much for being here and sharing all your guidance and your knowledge to um everyone. So I hope that um uh learners here would be able to um apply um your uh guidance and your advice and move forward in their career. Any any last word, any piece of advice um yeah for Yeah, thank you. I thank you so much for those kind words and you have been an amazing co-host. I I love presenting this with you. This has been fantastic. Uh, final words, AI's not going away. Jump in, get really familiar with this and do as much as you can with real world projects. That would be where I'd guide you. Brilliant. Okay, with those wise words, we're wrapping up this session. Uh, once again, if anyone has any concerns or any questions that we were not able to address or even any feedback to share, please write to us at [email protected]. Um and yes, thank you so much everyone for spending the last one hour with us. We hope to see you soon in uh our future webinars. Thank you everyone. Thank you Warren. Thank you.