How do you become an AI Engineer?
- Authors
- Name
- Amit Shekhar
- Published on
To become an AI Engineer, we learn to build products on top of existing AI models using APIs, RAG, agents, evaluation, and deployment. We prove those skills with a few working projects that we build with a coding agent and can explain end to end.
In this blog, we will learn about how to become an AI Engineer. We will also see what an AI Engineer does, what we need before we start, the skills to learn in the right order, what projects to build, how to show our work, and how to apply for the role.
We will cover the following:
- What does an AI Engineer do?
- Prerequisites before we start
- Skills to learn in order
- What to build
- How to show the work
- How to apply for the role
I am Amit Shekhar, Founder @ Outcome School, I have taught and mentored many developers, and their efforts landed them high-paying tech jobs, helped many tech companies in solving their unique problems, and created many open-source libraries being used by top companies. I am passionate about sharing knowledge through open-source, blogs, and videos.
I teach AI and Machine Learning at Outcome School.
Let's get started.
What does an AI Engineer do?
An AI Engineer builds products on top of existing AI models. The model already exists, and the AI Engineer connects it to real data, real tools, and real users, and makes sure it works reliably in production.
An AI Engineer does not usually train a model from scratch. Training a model from scratch needs a huge amount of data and computing power, and a research team. AI labs do that work. An AI Engineer uses the result of that work through an API.
Let's say a company wants an assistant that answers questions about its own product manuals. The model already knows how to read and write. But, it does not know the company's manuals, it cannot look up an order, and it sometimes makes things up. The AI Engineer feeds the right manual pages to the model, gives it a tool to look up orders, checks the answers for mistakes, and deploys the whole thing so that thousands of customers can use it.
So, the AI Engineer owns the following:
- The prompts that tell the model what to do
- The RAG pipeline that gives the model the right information
- The agent loop that lets the model use tools
- The evaluation that tells us if the system is good or bad
- The deployment that makes it fast, cheap, and safe for users
The nearest role is the ML Engineer, who trains and tunes models on data. An AI Engineer builds the product around a model that someone else has trained.
Prerequisites before we start
We need very less to start. Let's see what is needed.
First, we must be comfortable with one programming language. Python is the most common choice because most AI libraries and examples are in Python. An AI Engineer does not type every line of code. We work with an AI coding agent such as Claude Code. We describe the task, the coding agent writes the code, and we review, test, and fix it. So, we do not need to memorize every library. We need to be able to read code, understand what it does, and catch what is wrong in it.
Then, we must know the basics of how a web service works: requests, responses, and APIs. Every AI product is built on this pattern because the model itself is called through an API.
After that, we must know how data is shaped: lists, tables, key-value pairs, and JSON. The model sends and receives text, and we convert that text to and from these shapes all the time.
Finally, we must have used a chat product based on an LLM as a normal user, and seen it answer well, answer badly, and make things up. This experience is the starting point for everything we build.
We do not need deep mathematics, a research degree, or experience in training models. These are needed for research roles, not for this role.
A quick note for you
No matter which tech domain you work in, get familiar with these topics:
- LLM
- RAG
- MCP
- Agent
- Fine-tuning
- Quantization
We put it all together in one video:
AI Engineering Explained: LLM, RAG, MCP, Agent, Fine-Tuning, and Quantization
No need to stop reading - bookmark it and watch later when you get time. Future you will thank you.
Now, let's get back to the topic.
Skills to learn in order
Each skill builds on the previous one, so the order matters.
Step 1: We learn how an LLM works at a high level. An LLM (Large Language Model) is a model that reads text as tokens and predicts the next token, one at a time. We must understand four things: tokens, the context window (how much text the model can see at once), temperature (how random the output is), and the fact that the model predicts rather than knows. This last point explains why the model makes things up.
Step 2: We learn to call a model through an API. We ask the coding agent to write a small Python script that sends a prompt to a model and prints the reply. Then, we review the script line by line, run it, and change the prompt to see how the output changes. After that, we ask the coding agent to add structured output, so that the model replies in JSON like { "answer": "billing", "confidence": "high" }, and we verify that the JSON is always valid. We also learn streaming, which shows the reply token by token, and error handling for when the API fails.
Step 3: We learn prompt design. A prompt has a system part that sets the role and rules, and a user part that carries the actual question. We learn to give examples inside the prompt, to ask for a fixed output format, and to tell the model what to do when it does not know. A prompt that works on one input is not a working prompt. We test every prompt on a set of 20 or more inputs before we trust it.
Step 4: We learn RAG. RAG = Retrieval + Augmented + Generation. The model cannot see our private documents, so we retrieve the relevant pieces first and add them to the prompt. To do that, we split documents into chunks, convert each chunk into an embedding (a list of numbers that captures the meaning), store those in a vector database, and at question time find the chunks closest to the question. We direct the coding agent to build this pipeline, then we test it with real questions and check whether the right chunks come back. Most RAG problems are retrieval problems, not model problems.
Step 5: We learn agents. An AI Agent is a system built around an LLM that can use tools. We give the model a list of tools, such as search_orders or create_ticket, with a description of each. The model recommends which tool to call and with what input. Our code runs the tool and sends the result back to the model, and the loop continues until the task is done.
We must remember that the LLM never runs a tool by itself. Our code does. We ask the coding agent to write the loop and the tools, and we add guardrails: a limit on the number of steps, a check on every tool input, and a clear stop condition.
Step 6: We learn evaluation. We build an evaluation set, which is a list of inputs with the expected outputs. We run our system on it and measure how many answers are correct. When we change a prompt, a chunk size, or a model, we run the evaluation again and compare. For answers that are not simple right or wrong, we use another LLM as a judge with a clear rubric, and we check a sample of its judgments ourselves. Without evaluation, we cannot tell whether a change helped.
Step 7: We learn deployment and cost. We put the system behind an API, add logging for every request and response, measure latency, and track token usage, because tokens are what we pay for. We learn to cache repeated prompts, to pick a smaller model for easy tasks and a bigger model for hard tasks, and to add a fallback when the main model is down.
Across all seven steps, the real skill is the same. We describe the task clearly to the coding agent, we review what it produces, we test it against an evaluation set, and we design the system around it. The coding agent writes the code, and we are responsible for whether it is correct.
Want to become an AI Engineer?
These are the skills this role needs, and we teach them in our AI and Machine Learning Program at Outcome School:
- RAG
- Vector Databases
- AI Agent
- Tool use in Agents
- Evaluation of LLMs and Agents
- Model Deployment and Serving
We take you from the fundamentals to building real AI systems, step by step.
What to build
The best way to learn this is by building. Three projects are enough if each one is complete.
Project 1: A question answering system over our own documents. We pick a set of documents we know well, such as a product manual or a set of lecture notes. We direct the coding agent to build the full RAG pipeline: chunking, embeddings, a vector database, retrieval, and the final answer. This proves we understand RAG. We measure retrieval accuracy, which means how often the correct chunk appears in the top results, and answer accuracy on an evaluation set of 50 questions. We compare two chunk sizes and write down which one won and why.
Project 2: A support agent with tools. We build an agent that can look up an order, check a return policy, and create a ticket, using a small fake database. We direct the coding agent to write the tool loop, the tool functions, and the guardrails. This proves we understand agents and tool calling. We measure the task completion rate, the average number of steps per task, and how the agent behaves on inputs outside its scope, such as a user asking for medical advice.
Project 3: An evaluation harness for Project 1 or Project 2. We build a script that runs the whole evaluation set, prints a score, and highlights every answer that changed since the last run. We add an LLM judge for open-ended answers and verify a sample of its judgments ourselves. This proves we can tell whether a change made the system better or worse.
For each project, the README must contain the problem, a diagram of the system, how to run it, the evaluation results as a table, and a short section on what failed and what we changed because of it.
How to show the work
We make the work visible in four ways.
First, we push each project to a public GitHub repository with the README described above.
Then, we record a short demo video, two to three minutes, where we run the system, show one success, show one failure, and explain what we would fix next.
After that, we write one blog post per project. The post explains a single decision we made, such as why we chose one chunk size over another, with the numbers that backed it.
Finally, we keep the evaluation results in the open. A table that shows accuracy going from 62 percent to 81 percent after a change tells a hiring manager what we can do.
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How to apply for the role
The same job is posted under a few titles: AI Engineer, Generative AI Engineer, LLM Engineer, and sometimes AI Agent Engineer. Generative AI Engineer is the same role with a different name. We search for all of these.
When we read a job description, we map every requirement to one of our projects. RAG maps to Project 1. Agents and tool calling map to Project 2. Evaluation maps to Project 3. If a requirement maps to nothing, that is the next thing we learn.
On the resume, we lead with the projects, not the courses. Each project gets two lines: what it does and what we measured. "Built a RAG system over 400 pages of manuals, retrieval accuracy 81 percent on a 50-question evaluation set" is a line that an interviewer can check.
The interview usually has a practical build round. We are given a small task, we work with a coding agent to build it, and then we explain what the code does and how we verified it. The interviewer is checking whether we can direct the agent, review its output, and catch its mistakes. Then, there is a design round where we are asked how we would build an AI feature for the company, and we talk through prompts, retrieval, tools, evaluation, and cost. Finally, there is a conversation about trade-offs, such as when to use a bigger model and when a smaller one is enough.
If we are already a software engineer, the shortest path is inside our current company. We pick one feature that an LLM can improve, build it with a coding agent, add an evaluation set, and ship it. A shipped AI feature in a real product is the strongest project we can show.
Now we must have understood how to become an AI Engineer, what to learn in order, what to build, and how to present that work when we apply.
Frequently Asked Questions
Which programming language should we learn for AI engineering?
Python is the most common choice, because most AI libraries and examples are in Python. We do not need to memorize every library, because a coding agent writes most of the code. We need to be able to read code, understand what it does, and catch what is wrong in it.
Is a Generative AI Engineer the same job as an AI Engineer?
Yes. Generative AI Engineer is the same role with a different name. The same job is also posted as LLM Engineer and sometimes AI Agent Engineer. So, we search for all of these titles when we look for openings.
What happens in an AI Engineer interview?
An AI Engineer interview usually has a practical build round, where we work with a coding agent to build a small task and explain how we verified it. Then, there is a design round about building an AI feature for the company. Finally, there is a conversation about trade-offs, such as when a smaller model is enough.
What is the shortest path for a software engineer to become an AI Engineer?
The shortest path is inside our current company. We pick one feature that an LLM can improve, build it with a coding agent, add an evaluation set, and ship it. A shipped AI feature in a real product is the strongest project we can show.
That's it for now.
Thanks
Amit Shekhar
Founder @ Outcome School
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