How to get 36 LPA job opportunities as an AI Engineer with 4 YOE. Just like me. No BS Guide...

2026年9月27日9月27日

文章目录

Who is this guide for?

It’s For those who have 3+ years in any professional programming experience in industry, or better if experience in data field like data analyst, data engineer, ml engineer, and wants to transition to Genai role.

For freshers, there are only a few companies which have open Genai positions for freshers. Freshers should:

Or get an internship position as an AI or Genai engineer, if they have relevant skills.
If freshers dont have relevant skills for GenAI, then they should try to get ‘easy to get’ roles in the data field which are data analyst and data engineering, learn Genai parallelly, get Specialized Certifications, and build portfolio projects. This way it is really easy to get a Genai role after a couple of years of experience in the data field.

Now, Lets start with is there even a demand of AI engineer? Or AI is taking the jobs???

The truth is: AI isn't taking jobs at all, atleast not for skilled engineers. If AI is taking the jobs, then how come I get 10+ calls from Naukri.com every day??? Yes, 10+ calls every day.

So lets not waste time and get straight into points:

1) Prepare for GenAI Engineer role:

You might have little to no idea how much GenAI Engineer role is in demand.
Prepare for the role to pass the interview. That’s the key. You dont need to swim in the whole ocean, you just need to learn enough to be able to cross the swimming pool.
If you are in your current job and get any chance to work in any GenAI project, do it. It will help you make the switch to a complete GenAI role.

2) What to prepare:

2.a) Design Principles and Patterns:

Once you get the job, every single function you implement needs to follow Principles and Patterns. This is what prevents your code from becoming spaghetti.

Brush up OOPS and practice before learning principles and patterns. As someone with 3+ years of experience, you should be knowing this beforehand.
Learn SOLID principles (the most important one), and other principles like DRY.
For patterns, there are 23 patterns divided into 3 categories, but you don't need to learn all 23, you just need to learn very common patterns like:

1) Creational Patterns:

Factory
Singleton
Builder

2) Behavioral Patterns:

Strategy
Observer
Command
State

3) Structural Patterns:

Adaptor
Decorator
Facade

2.b) RAG:

Study and practice RAG to pass the interview. Study the important concepts that is asked in technical interviews regarding rag. Different scenarios are given to you and you will be asked what action you will take and you must prepare for that. The scenarios such as:

How would you trace the issue if the end answer generated by LLM from your RAG pipeline is wrong,
If documents contain Images, tables and text, how would you embed it and what would be your retrieval pipeline.
Ask chatgpt to prepare you such interview questions.

2.c)

Practice LLM finetuning, study concepts like Quantization, Lora, FPs and ints, how to prepare the dataset for finetuning.

2.d)

Study Vector DBs. Questions like types of DBs for RAG, when to choose what kind of dbs and other scenarios.

2.e)

Practice Cloud Deployment in AWS. Read and practice how you can deploy RAG pipeline in aws. What are the services you might need. AWS is the most demanding cloud services in 2026. And if you have hands-on experience in AWS, this is the biggest advantage you will be having right now.

2.f)

Practice Basic DSA. See, for any job interview process, there are 2 kinds of DSA round:

Dedicated coding round which is hard mostly. But nowadays many companies for this role are not taking such dedicated coding rounds. They prefer to give you a simple coding question while in the technical interview round itself (which is my next point).
Coding questions asked while technical interview is going on which is very easy to crack. For GenAI roles, most of the time you will face this type of small DSA questions. You will be giving technical round and after few minutes, the interviewer will ask you to open any notepad or terminal, and they will give you just 1 or 2 simple DSA questions to solve. Why is this easier than a dedicated coding round? Because this happens during technical interview where they need to ask other important questions, they make sure to give simple DSA questions to you.

2.g) Topics to practice in DSA:

DSA can be divided into 3 parts: Data structures, algorithms, and patterns. Data structures which are often asked are: Arrays. HashMap. Strings.
Algorithms you need to prepare are: Linear search. Binary search. Sortings.
And to solve questions related to these data structures and algorithms, you need patterns which are Two-pointers . Sliding window.

This is all you need to solve those coding questions. Ofcourse, study basics like Time and Space complexity for god sake lol.

2.h)

Practice 20-30 easy and some 5-10 medium questions in leetcode. It will take you couple of days only. These much leetcode is all you need to get the GenAI role now.

2.i) Frameworks to must learn:

LangGraph (mainly) and LangChain for AI workflow, Crew.ai for ai agents, FastAPI.

These are mainly required. Learn about their architecture and how they work. In interviews, I have been asked many times to tell the difference between langgraph and langchain and when to use what. Companies generally prefer langgraph.

2.j) Build AI Agents:

Build some automation using AI agents via coding. This has a huge added benefit that will help you stand out. You can use frameworks like crewai.com to build ai agents via coding. Example project you can build or work on is Agentic RAG, where if an external source of information is needed then an AI Agent will be called and it will be using a web-search tool to extract external information from the internet and pass it further.

3) What you dont need to learn/study/practice:

3.a) Maths:

This is for researchers, not for engineers. You are preparing for Engineer role, not for research. You don't need Maths at all. If you know Maths, that’s an added bonus, but not mandatory.

3.b) Mastering DSA:

Don't waste your time. The topics I have listed for which you need to practice DSA to clear technical interviews is enough. But if you are good at DSA, then you can also pass the dedicated coding round.

3.c) Watching Long Tutorials:

Don't do this at all. You will fall into tutorial hell. Read the Docs. Learn from the docs. You are an engineer, you don't need tutorials primarily, you can watch tutorials at last. Docs, articles, ChatGPT, is all you need to learn and study.

4) Hacks:

4.a) Take help from ChatGPT:

ChatGPT has skyrocketed my learning curve by 10x. The topics I used to take weeks to learn have been reduced to a day of learning. Ask every small detail, ask it as a beginner, ask it to explain in however way you can understand. Just keep asking.

4.b) Read the docs:

Most of the beginners prefer watching tutorials instead of docs. But reading docs is a skill which gives exponential results. I do admit that not every framework/library has comprehensive docs, and docs may be overwhelming at first, but once you start building any project by reading docs rather than by watching tutorials, you will master that framework/library much faster, in depth and build confidence. You will not be bothered about forgetting the syntax or functions, one look at docs and that will be enough.

4.c) Watch tutorials only when:

Watch tutorials to learn theories, and not framework/library. For framework/library, refer to the docs as stated in (4.b).

5) Make a profile in Naukri.com and optimize it for GenAI role. I'm getting 10+ calls everyday as recruiters are very actively searching for candidates who are into genai roles, and they are searching on naukri.com. Put all the relevant keywords wherever asked to fill.

6) Make a resume for GenAI engineer role and put relevant keywords there too.

8) MUST KNOW THING for SCREENING CALL:

When any HR calls you for a screening round, and ask you if you have 5 years of experience in GenAI, just say YES. Never try to explain to an HR that genai is just 3 years old, this and that. Never. Whatever HR asks if you have worked, just say YES. They are HR, non-technical people, they don't know anything at all, they will simply reject your profile if you don't meet the criteria. The first step is to pass your profile for further rounds, so this HR screening call should be the easiest for you, just say YES. You can save your technical knowledge for the technical rounds haha, but for HR screening call, just say YES and let your profile go for further rounds.

9) Career Growth:

9.A)

For this role, industry standards are usually 30% hike on current CTC, take it. Work on it for a year, master yourself in LLM Inferencing, Finetuning LLM and RAG, keep practicing DSA, keep posting on LinkedIn for authenticity. After few months only, you will either receive inbound DM in your LinkedIn for a new opportunity, or incase if you apply for another job opportunity then you will get it easily. Don't be afraid of making the Jump/Switch every 6 months to 1 year.

9.B)

Contract Work is a viable opportunity for this role, you get more payment in-hand as contractors get more. Just make sure you opt for PF even in contract, as in future if you want to switch, be it for another contract role or as a full-time employee, then every company asks for PF no matter what.

10) Alternative Path - Azure GenAI role:

Many companies are hiring GenAI engineers to work in their Azure environment. They must know Azure ecosystem and services such as Azure OpenAI, Foundry, etc. Azure provides a very easy workflow to build GenAI products, and many companies are actively choosing this workflow over code-based workflow. So do a couple of relevant Azure certifications related to GenAI and you can get this role very easily. Demand is growing, supply is less, and Payrate is good here too.

11) Life Hack – RetardMaxxing:

This is a life advice for you. Alot of us analyze too much, read many guides, seek alot of advice, plan too much, but never start at all. Or even if we start, we stop too early. That’s the problem even i have dealt with.

Retardmaxxing is a lifestyle where you be a retard and literally stop thinking and start doing. For example, don't plan for leetcode too much, just do it. The learning curve is exponential and so does the results. If your first question is taking 1 hour to solve, that doesn't mean your 30th question for same topic will take 1 hour, it will take 20 minutes max, or may be 15. So just stop thinking and just start doing.

全文完

沿着这个主题继续读

从一篇文章，找到相关的教程、经验和阅读路径。