The Complete AI Engineer Roadmap: From Zero to Job-Ready in 6-8 Months

The field of AI is evolving incredibly fast. Every month brings a new SDK or framework release, and big tech companies launch new LLMs regularly. With so much happening, it is normal to feel confused about what skills to learn and in what order.

When you search YouTube, you will find many roadmap videos, but some of them are created by people without real industry experience. I wanted this roadmap to be different and grounded in reality. So, we analysed more than 700 recent AI engineer job postings to understand which skills are actually in demand. We combined that with our own experience working on over 25 AI projects at AtliQ in the past two years.

This roadmap gives you a clear week-by-week study plan, along with free resources and checklists. It requires 4 hours of daily study for 6 to 8 months. If you are looking for shortcuts, this is not the right place.

Understanding AI Engineer Salaries and Job Market

Before diving into the roadmap, it helps to understand the job landscape.

In India, AI engineer salaries on Naukri.com range from 3 lakhs to 5 crores (typically for Senior Directors), with most roles falling between 6-15 lakhs. In the US, the median compensation is around 152,000 dollars. In Silicon Valley, it can reach 400,000 to 600,000 dollars a year.

Currently, Naukri lists about 37,000+ AI engineer roles and around 111,000+ ML engineer roles. This roadmap is suitable for anyone aiming for AI engineer, ML engineer, or GenAI engineer roles.

Are You Suited for This Career?

Once you decide that AI engineering sounds good, check whether your natural skills align with the role. We've created a suitability test that asks about your inclination toward math, your learning attitude, and more. Based on your answers, it tells you the percentage match. It will help you understand if this career is right for you.

Take this test to know your suitability: https://codebasics.io/survey/find-your-match-ds

The Three Categories of AI Engineers

Although many job titles say “AI Engineer,” the actual work usually falls into three categories.

1. Integrator

In this role, data scientists train the models and the integrator deploys them. The integrator handles infrastructure for hosting ML models, which includes managing cloud resources, CI/CD, Docker, and related tools. Essentially, the model is trained by someone else, and the integrator ensures that it runs smoothly in production and connects well with the rest of the systems.

2. Builder

AI Research Engineer: People who like math, algorithms, coding, and love building models usually fit this role. Big companies like OpenAI and Google hire AI research engineers to build new LLMs and develop new training methods.

Applied Scientist: This role is product focused. For example, at Amazon, applied scientists build the recommendation systems you see on the website.

Data Scientist: This role is business focused. Data scientists often work in banks, healthcare, and smaller companies where custom statistical models and deep learning models are needed.

3. All-rounder

This role combines integrator and builder responsibilities. They know enough about data, Machine Learning, Deep Learning, pre-trained models, deployment, integration and business problem solving to deliver end-to-end AI solutions on their own.

Small companies and startups rely on them because project needs vary and they need someone who can ideate, build, and ship quickly without depending on multiple specialized roles. At AtliQ, we often need all-rounders because project requirements vary widely.

What Top Employers Look For

Here is what Karan, the AI head and CEO at AtliQ Technologies, looks for when hiring:

1. Breadth of AI knowledge: Not only GenAI. A good engineer should understand statistics, probability, math basics, rule-based systems, statistical ML, and deep learning. Only knowing GenAI is a red flag.

2. Prototyping ability: You should be able to build a quick prototype in a couple of days to check whether a problem is worth solving.

3. Communication skills: Clients often experiment with ChatGPT before talking to you. You must communicate clearly, clarify doubts, and keep expectations aligned.

Real-World Example: The Handyman Approach

We once worked on a log classification project in the finance domain. Initially, we thought of using GenAI for everything. It worked, but the API costs were high and the explainability was low.

So we split the problem into smaller parts.
• About 80 percent of logs had fixed patterns, so we used Python regular expressions.
• About 15 percent had partial patterns, so we used BERT encodings and models like XGBoost.
• The remaining 5 percent did not have enough training samples, so we used LLM classification.

This hybrid approach combined rule-based systems, statistical ML, and GenAI. In real projects, one single model almost never solves everything. A good all-rounder knows how to choose the right tool at the right time.

To access the complete roadmap along with the downloadable AI Engineer Roadmap 2026, LinkedIn checklist, and Resume checklist, use this link:
https://codebasics.io/resources/ai-engineer-roadmap-2026