AI & ML Career Roadmaps

Step-by-step AI & ML career roadmaps: the skills to learn, in what order, and what each stage of the path is for.

Browse all career roadmaps

1. AI Agent Engineer

   Design and operate autonomous tool-using agents that plan, act, and self-correct across real systems. High leverage, high ambiguity, and one of the fastest-moving specialisations in engineering right now.

   AI & ML · advanced · 14 min read

2. AI Engineer Building LLM-Powered Products

   Design, ship, and operate LLM-powered features and products end-to-end—from prompt architecture to production inference—at the intersection of ML engineering and software craft.

   AI & ML · intermediate · 14 min read

3. AI-Assisted Software Developer

   Build production software using coding agents, LLM APIs, and prompt-driven workflows as everyday tools. This path is worth walking because AI amplifies output but demands sharper engineering judgement, not less.

   AI & ML · beginner · 14 min read

4. AIOps Engineer

   AIOps Engineers apply ML to IT operations and observability, turning alert floods and telemetry noise into actionable signals. The path is worth walking because the gap between SRE instinct and production ML is still wide enough to command a premium.

   AI & ML · advanced · 14 min read

5. Computer Vision Engineer

   Builds, trains, and ships perception systems that make sense of images and video at scale. One of the few AI specialisations where low-level intuition still beats prompt engineering.

   AI & ML · advanced · 14 min read

6. LLM Application Engineer: RAG, Agents, Evals and Context Engineering

   Builds production LLM systems that retrieve, reason and act reliably. High demand, fast-moving, and the gap between demo and production is where this role lives.

   AI & ML · intermediate · 14 min read

7. Machine Learning Engineer

   MLEs own the full arc from raw data to production model: training pipelines, serving infrastructure, and the feedback loops that keep models honest. The path is hard to automate away because it sits at the intersection of systems engineering and statistical judgment.

   AI & ML · intermediate · 14 min read

8. MLOps Engineer

   MLOps Engineers own the full lifecycle from experiment to production model: training pipelines, serving infrastructure, monitoring, and the feedback loops that keep models honest. The path is worth walking because it sits at the highest-leverage intersection of ML and platform engineering.

   AI & ML · advanced · 14 min read