AI Engineer Roadmap for Software Engineers

Key Takeaways

Software engineers are most suited to become AI engineer because AI engineer is equal to software engineer with great backend skills plus LLM and AI model understanding plus AI system design. Many software engineers today want to transition to AI engineering role because that's where the future is. They start by becoming integrator which means they will call LLM APIs to build AI features into current software and later on they can learn how to fine-tune LLM models or even build ML and DL models from scratch. Before we start a short introduction on myself I am Daval. I have worked with big tech companies like Bloomberg and Nvidia for many years. At present I am the co-founder of AI consultancy company at lake where we have transitioned many of our existing software engineers into AI engineers role and that helped us implement more than 25 AI projects for our clients based in US and UAE. Whatever I'm going to share today is based on real life industry experience and today I will show you a most practical road map which will help you learn AI engineering. This contains weekby- week study plan with free learning resources. Here is the PDF of the road map. This is built exclusively for software engineers with minimum 2 years of software development experience. It is a fast-paced program which you can finish in 2 months using free resources. We are going to assume you are spending four to five hours 5 to 6 days a week. Now exit time that you take to learn all these skills depends on your learning speed, your dedication and few other factors. This includes both technical skills and soft skills. In the AI era, soft skills are equally important folks. Okay. In week one, you will learn AI and Python fundamentals. Python is the programming language for AI. There is no doubt about it. Here I'm showing you a code snippet of how you can call open AI API from Python code. Python is something that people use across the industry. Hence, you need to know all these fundamentals. Okay, Python data types, functions, exceptions, loops, control flow, all of these. Now, as a software engineer, you already know about if, for loop, object-oriented programming and so on. Now you can use this transfer learning. You need to map the concepts that you have learned in different programming language. Let's say you know Java, you have worked in Java. You need to map those concepts to Python. That way your learning becomes very very easy. Fast API is super important. It is used to build a backend. Now as a Java orn net developer, you would have built back end already. So once again you need to map those concepts to quickly learn all these things. You don't want to spend six months learning Python and fast API folks. We live in the world of vibe coding AI first engineering. You should be uh willing to use this cloud codegp etc tools to write code faster. Okay. Forget syntax. You don't have to remember syntax now nowadays. Focus on fundamentals. Focus on building. Okay. Now once you have cleared some of the Python fundamentals, once again you don't need to go too much in depth in Python. Okay. Just map your programming concepts to Python. Then uh this is the most important tool folks. Grock. Okay. So Grock is a tool that will allow you to call LLM freely. So in your learning phase you don't want to spend money and this is an amazing tool for learning folks you can call all these models you can create free API key and you can uh build AI projects for free okay so gro is something very important you can register your account on this platform and they have documents if you want to call it from Python it's Super easy. Uh so let's see. Uh this is Grock. Okay. So client import uh this is I think open API code. But if I go to gro see gro API key you have to first get and then you will use this gro module and this is how you call uh let's say this llama model for whatever use case you want to call it. Okay. So you have to build uh this code using grock so that you know how to call LLM APIs from your uh Python code. Then you have to be very strong in V coding. I call it AI first engineering because V coding sounds like trivial hobby thing. As a software developer you are building complex systems where you will use cloud. Claude is my favorite tool by the way. Uh I use claude code all the time and recently we have published crash courses for all these tools. So here is a one for claude which we made it live just yesterday. Then we have one for Google anti-gravity cursor and so on. These tools are easy to learn for you as a software developer. But there are some V coding principles that you need to know such as what is skills.mmd file, what is claw.md file, how do you effectively supply context and optimize on your token usage. All this discipline you need to learn which I have mentioned in those tutorials. Then you need to know generative AI basics. What is geni agent? What are agents? What are different configuration parameters for LLM such as all of these? Okay. What is embedding vector database? Use cases of vector database. And when I talk about vector database, see chromad is an open source. So that is where people start their learning journey. But when you work in the industry, people don't use chrom, they use the commercial vector databases such as quadrant, pine cone and so on. So it is better you get hands-on experience on this complex vector databases. I have mentioned uh YouTube tutorials. These are all free learning resources folks. You don't have to spend any money. And as you see, we have Python tutorials. We have uh tutorials on on understanding the theoretical fundamentals for LLM and JNAI and so on. And the most important point here is folks, you should use cloud code or chat GPT as your personal tutor in this entire upskilling journey. Okay. Now, let's talk about soft skills. In the modern times what has become important is you build a personality where you are a write mix of soft skills and technical skills. In the previous era where we used to write all the code by hand technical skills mattered a lot. Nowadays you can write code using clawed code. So the importance of technical skills is actually reducing and the importance of soft skills is actually increasing. So let's say previously if you have 100% tech skills okay you will see these geek programmers working in a company who doesn't know even how to talk okay who are very weirdo type of personality and these people will survive okay so they will have 100% of tech skills and let's say 0% soft skills okay or let's say even if you have let's say I should not make it so extreme like everyone has some substance so these kind of people were surviving now in the age of claw code these people are going to lose their job folks there is no doubt about it now we live in a time where you have let's say even if you have 30% of soft skills or tech skills and 70% of soft skills you will actually become invincible okay and When I say tech skills, it includes uh coding, cloud knowledge, architecture. You should be able to build resilient AI systems. You should be able to optimize your token cost. You should be able to figure out uh the retry logic and you know how to scale your systems to millions of users. Okay, those skills are going to matter. But even more important thing is soft skills and therefore we have this section where uh these soft skills uh include the following. Okay. So I'm going to just list it down. So the first thing is stakeholder management then communication. Communication is required for every other soft skills including stakeholder management. Uh then presentation. See all these skills are going to matter a lot folks. Okay. And LinkedIn helps you build the fourth skill which I forgot to mention which is personal branding. You need to build personal brand. You need to have distribution folks. Otherwise you are not adding any value. You know cloud code can do coding. It can do architecture. So what value are you adding? Okay. So let's talk about personal branding. Here LinkedIn is the tool from where you can stream your credibility. You build your credibility somewhere else. Let's say by building open-source project, by competing in some uh tech competitions or by speaking at a tech conference, you're building your online credibility. Then you stream it through LinkedIn, through Twitter. Okay. So, Twitter is actually even more important than LinkedIn because most of the AI news updates, most of the activities uh or exchanges around AI are happening on Twitter. Okay? So, on LinkedIn and Twitter, first create a professionallook profile. Okay? And you can use this checklist folks. We have built a checklist. You see this checklist. So once you go through all these points at the end you would have created a professionallook LinkedIn profile. I know many software engineers who don't even have LinkedIn profile or if they have it they will only use it when they are switching the jobs. Okay that attitude is not going to work folks. You have to be active. You have to be part of the community if you want to stay relevant. Okay. In terms of assignments in this week, you will build a simple fast API server to display motivational quote. Okay. Then you will build a simple search engine using Chroma or Quadrant and you will build professional looking LinkedIn and I would say X profile. Okay. Now why LinkedIn and X is important? Because it's like if you have this kind of samosa shop in a small village, although you make very nice samosa, you will not make lot of business. But if you move the same shop to a busy market in Delhi or Bangalore or New York, you're going to make lot of money. The product is same. The product here is you folks. Okay? You are same but you are expressing yourself on LinkedIn and X which is a busy street and more people are noticing you. Okay. In week two you are going to work on rag fundamentals and lang chain. In most of the projects that we have worked at in atlic technologies I would say 60 to 70% projects are rag projects. RAG means retrieval augmented generation where you can point your LLM to your internal organizational data store and it can work on that knowledge base and it can do various things for you. So here you will learn the theoretical foundation which is what is rag, how to build rag pipeline, document parsing and chunking using dock link, lang chain basics, chat models and all of this and we have some free learning resources here. So just just look at them. Okay, there's a dock link tutorial by Redhead here. In terms of soft skills, see creating a profile alone is not enough. you have to start following the prominent uh AI personalities such as Andre Karpathi for example. Okay. And when they make a post, you engage in their post. You write thoughtful comment. Now these people have thousands and thousands of people who are in the AI industry. They are going to notice your personality. Okay? Maybe Andre Karpati will respond to your comment and other people will notice you. That way uh you are building this micro relationship when you engage in somebody's post asking thoughtful questions. You are uh leaving a positive impression on their mind and this relation that you build over a period of time is going to help you when you want to transition full-time to AI engineer role or when you want to switch your job or even when you want to do your own business. Okay, so this is super important folks. you start commenting meaningfully on AI and career related post. You can do the same thing on LinkedIn as well because remember that online presence is is a new form of ré. Don't go for old ways where you are using your réumé to apply in any job. It's not going to be that effective. Now these software engineers are working professionals and sometimes they prefer live learning. They don't like watching this YouTube videos with ads and all that. You know the main issue is the discipline. So if there is a live learning they will appreciate. If there is a live doubt clearing they will appreciate. And that is the reason after so many people requested this. We have launched live AI engineering cohort exclusively for software engineers with minimum 2 years of experience. It is 75 days program which will quickly help you gain all these AI engineering skills. There are going to be sessions on weekends. It starts on March 7, 2026. You can enroll even later also. The second cohort will start after after the first cohort is over uh in 2 months. If you're interested uh if you want to know more uh check out this page uh all the details are included. Now when it comes to business fundamentals um you want to follow some uh resources so that you can gain business knowledge. When I was at Bloomberg we had many data analyst who will have this CFA degree who will have domain specific degree. So even if you want to get domain specific degree let's say in finance or healthcare it makes total sense because we will see this time where the role of and it is happening okay it's not future actually it's happening even right now the role of product uh owner or let's say business manager and software engineer is merging and we are seeing this new role called product engineer Okay, product engineer is 50% or let's say technical skills and 50% the product skills which is what your product owner or what your business manager is doing today. Right? It requires soft skills first of all and it also requires domain knowledge and that is the reason we have included some resources where you can build your business fundamentals. In terms of assignment, you will code a rag pipeline in pure python because frameworks like lang chain they keep on making updates. It is helpful if you can build uh this rack pipeline purely in python and folks you can use clawed code for this. It is totally okay but just understand the fundamentals you know try to understand code also just in case if it makes mistakes. It is helpful that you have understanding of the code and after you are done building this rag pipeline in Python you will code a simple lang chain rag chatboard for healthcare sector let's say for finance sector okay you will also write meaningful comments on at least 10 AI related LinkedIn or x post and note down your key learnings from three case studies on think school or any other uh business uh school okay in week three you will work on agentic AI fundamentals. So here you will learn what is AI agent, react loop, re reasoning plus action loop, building AI agents in lang chain, routing in AI agents, you know like how does the routing work? The way these AI agents work is for um given intent. So let's say if you are talking to an AI enabled customer care chatbot, it will have some kind of routing. It will detect your intent. Okay, you have technical query, you have query related to pricing etc. And based on that it will route. It will also route based on the complexity of the task. So if you are performing a simple task, it will use some uh cheap model. But let's say if you are asking deep question which requires thinking, it might use OPUS 4.6 thinking. Okay. So that kind of routing is required. Then this is super important folks. AI agent security. uh in my company at when we get clients the first question every single client has asked is what about security is my data secure I don't want to send my data to claude server okay there are ways to handle security so you will learn the fundamentals of that in this week guardrails is kind of overlapping with security and then you will also evaluate your system AI systems are probabilistic by nature. They're not like software where the things are predictable and deterministic. Here they are probabilistic. Hence you need a different paradigm for evaluation. If you're writing code you can write your test cases and you can say the output should be exactly this. But in in case of AI output is going to vary. See if you ask same question to chat GPT five times it gives you different output. Okay. That is the reason you need to know the evaluation techniques. Okay, we have mentioned the learning resources here in terms of soft skills. I have noticed that I would say majority of the software engineers are not good at presentation. If you ask them to present something, they will bore you to death. Okay? And that is the reason you need to watch this presentation called death by PowerPoint. It outlines simple techniques to make your presentation effective and compelling. You should be doing a storytelling. You should be engaging with the audience. Don't put your boring technical diagrams in PPT and you know make the people in audience go sleep. So folks, I would say this timeless skill is super duper important. Here are the assignment. The second assignment is writing two meaningful post on AI tech topic. The reason I'm giving this assignment is uh look at this job post. Okay, this is a real job post where they say effectively communicate with key stakeholders in written oral and presentation format. Okay, so there are many job post where even I have read you need to have good education skills. We have experienced this in my company where we had one project where client came and he was a business person but nowadays all these business people will talk to CH GPT and they come up with the technical architecture diagram. They tell us what kind of technical architecture we should be using. We have entered this era where business manager has access to these AI tools and they think they can build even technical architecture. Many of them can surely build but the problem is a business stakeholder having half cooked knowledge and a very high confidence. How do you deal with them? You want to talk to them so that you educate them in a right way without hurting their ego. It's very important. So, how to educate uh business stakeholders and nonte people is going to be an essential skill for you as an AI engineer. Okay. Now for that of course for that we have given this assignment which will help you build that skill. All right. Now in week four we will work on AI application observability and deployment and there you will cover lang. So if you talk about lang chain ecosystem we have lang chain which is a general framework we have lang graph which is to build a very customized agents and we have lang which is observability and monitoring platform. Okay. So you can track the the execution of your code. You can have traces, runs, threads, cost tracking and so on. Then you want to uh get clarity on how do you deploy agentic AI system to production because as I said AI systems are probabilistic. The deployment paradigm is little different than how you would deploy your regular software. Okay. AWS agent core is a platform uh that helps you deploy the agents into AWS cloud. We have a free tutorial here. See all these tutorials are free uh that you can learn. All you need is a laptop and a willpower folks. Now you want to start contributing to opensource as well because this adds to your online credibility. Whenever I'm hiring AI engineers for my team and if I see a resume where the first line is I have successfully contributed to opensource five PRs merged in this particular repository I will immediately call that guy for the interview and guess what I don't get so many of the these rums okay I get rarely I think 1% of rums that I review are having this kind of open-source contribution okay so if you want to uh stand apart in a competition Focus on this. Nowadays contributing to open source is again very easy. You can use chat GPD and claw code as your friend. Go to any repository. Okay, I'm going to mention some good repositories that you can start with and in this repositories you can search for a tag. Okay. So when you go to GitHub here I am on the GitHub of transformers which is an open source library and they have more than thousand pending issues. Now let's say you want to help them fix it. What you can do is you can search for good first issue. Okay good second issue things like that. And these are beginner friendly bugs that you can fix. Okay. I know there are two of them but you will find tons of other repositories. You can also search for good second issue and you can contribute it that way. Once again contributing to open source is easy. You don't have to be expert. All you need to know is how to use AI tools. Okay. And this is the assignment. In week five, you will focus on multi- aent systems and context engineering. In first four weeks, you have learned how to build AI agents, single AI agent. Now you are talking about multiple agents. Okay? And langraph is a framework that will uh let you customize things. It's like you have a DSLR camera and you are recording in auto mode. So that is using lang chain. But if you want to uh record in a manual mode, so many of these professional photographers they record in a manual mode. Uh and when you do that you get lot of control. So, langraph gives you that kind of custom deep control on building agentic system especially multi- aent system. Crew AI is another framework by the way. Okay. So, you'll learn all these topics and for context engineering also I have mentioned few of the topics and these are the resources you can follow. Most of these resources have high views, high engagement and uh good quality content folks. So we have carefully listed down all these uh resources. Okay. And these are the assignments. You can build a multi- aent system to automate marketing task and you can build an MCP server for a public API. Now comes week six. Here you are entering the world of advanced AI engineering topics such as optimizing the cost of AI applications, rate limiting, model selection and cascading response caching. If you keep on calling open AI API or cloud API on every single iteration, your bill is going to blow up. Folks, this is the experience we have at at where our clients are like, "No, no, I want to use the best model. Let's go with OpenAI." We start using it and when we deploy it in production and when they get a bill at the end of the month, they are blown away. Uh this is like AWS bill, right? Like AWS I sometimes feel like is like a black box. you don't have a way to track what kind of bill you are going to get. You know only when you actually have bill in your hand. Okay. So that can happen with this LLM APIs. Therefore from day one you should be optimizing your token uh cost and for that caching rate limiting model selection and cascading are the techniques that you can use. Then you have to explore the topic of multimodel rag especially in the fields of healthcare and manufacturing there is lot of usage of computer vision okay you are dealing with images and videos and so on and that's what multimodel means multimodel means not just text but audio video etc there might be a need where you have to fine-tune the LLM rag alone may not work you have to use Laura Qura etc Um and you can do all of that using this module or package or a framework called onslaught. Okay. And I have mentioned all these resources. Running a model locally is also very important. So we have bunch of clients from UAE and they have requirements that they want to run things locally. They don't want the data to go out at all. So you know you see all these businesses uh across the world where they're okay getting a computer getting bunch of GPUs in house but they don't want to send their data outside and in that case running models locally becomes important. Okay and Olama is one of those frameworks that helps you do this and we have a tutorial for this as well. In week seven, you will learn declarative AI using DSPY. DSPI is a framework that you can use to build AI applications using declarative approach. If you're a software programmer, you will know about declarative programming. This is the same thing but for AI. Okay. So, we have mentioned all the learning resources, assignment and so on. In week eight, which is the last week, you will learn how to deploy your models to production. uh you will use Azure and AWS which are two prominent clouds when it comes to building AI solution. Google is also important, Google AI studio etc. Okay. See the way I look at cloud is once you learn one cloud working on other cloud becomes easier. So you don't have to learn all three. Learn Azure which is very popular and then learn either AWS or GCP when you have time. Okay. You want to clear your fundamentals on resource groups, subscriptions, regions, storage, IM, etc. And here also you can use transfer learning technique. Once you know one concept, if you go to AWS or let's say GCP, they will have similar concept. They will just call it by a different name. All right? So look through these topics. Uh once again if you want to fast track your uh learning uh with live sessions then we have this particular boot camp. It's a live cohort so of course it's going to starts on 7 and it will end in few days. We'll close the enrollments. The link is in the video description below. Now here are some of the tips for effective learnings. You will have your software engineer friends with whom you are discussing over a coffee break what's going to happen to our job etc. And many of these people might be willing to learn AI. So why don't you make a group study group okay you can make a study group and you can learn together. That way you can have a weekly check-in. You hold each other accountable for the progress. You can share your learning your thoughts. it becomes so easy. It's like if I'm going alone to learn yoga, it's difficult. But if I'm going in a group, it's going to be easy. And you need to spend less time in consuming information, more time in digesting, implementing, and sharing. That is the rule of effective learning. That's all we had. I have attached this PDF file in video description below. If you have any questions folks, feel free to post in the comment box. We are going to respond to all the questions, okay? I will try my best to respond to all the queries that you're posting in the comment box below. I wish you all the best and thank you very much for watching.