Road Mates EnterHow to become an AI Engineer, phase by phase: what you do in each one, how long it takes, and the proof that shows you're done.
7 phases2–3.5 years23 resources
Writing Python daily, working through data structures, linear algebra, probability, and basic statistics with hands-on exercises. By the end, the person can write clean scripts, use NumPy and pandas fluently, and explain gradients and matrix operations.
You're done when you can show
A GitHub repo of completed Python and math exercises with working notebooks.
Resources
Stops drilling fundamentals and starts training, evaluating, and tuning models with scikit-learn on real datasets. Produces end-to-end notebooks covering data cleaning, feature engineering, cross-validation, and error analysis.
You're done when you can show
A public notebook showing a full ML pipeline with evaluation metrics on a real dataset.
Resources
Moves from off-the-shelf estimators to building neural networks in PyTorch, including training loops, CNNs, and transformers from scratch. Ends with reproducing a small paper or architecture and understanding fine-tuning.
You're done when you can show
A repo with a from-scratch transformer or paper reproduction and training logs.
Resources
Stops training models from scratch and starts building products on top of foundation models: prompting, RAG pipelines, agents, tool calling, and evaluation harnesses. Produces deployed apps with APIs, vector stores, and measurable quality metrics.
You're done when you can show
A live deployed LLM app link with its source code and an evaluation report.
Resources
Shifts from prototypes to reliability: containerizing services, adding monitoring, CI/CD, cost and latency optimization, and serving models on cloud infrastructure. Proves the person can run an AI system that handles real traffic and failures.
You're done when you can show
A repo with Docker, CI pipeline, and monitoring dashboard for a deployed AI service.
Resources
Stops building solo projects and starts packaging the portfolio, practicing ML system design and coding interviews, and applying or contributing to open-source AI projects. Ends with a signed offer for an AI engineering position.
You're done when you can show
A signed offer letter for an AI or ML engineering role.
Resources
Owning AI features inside a team: scoping problems, building evals, shipping models or LLM systems to users, and iterating on real usage data. Proves impact through features that move business or user metrics.
You're done when you can show
A launched feature writeup or internal doc showing measured impact from your AI system.
Resources
Walk this road with people on it.
Track each phase, post your proofs, and get matched with people in the same phase.