ML Engineer
A research-backed roadmap from Python foundations to production LLM systems — PyTorch, MLOps, cloud serving, LoRA fine-tuning, and inference optimisation in the exact order that 2026 hiring teams are looking for.
Python & Math Foundations
3–4 weeksThe technical bedrock of ML engineering — Python performance patterns, linear algebra for neural nets, and the probability/statistics that make model decisions principled rather than arbitrary.
Classical Machine Learning
4–6 weeksThe fundamentals every ML engineer must own — scikit-learn pipelines, gradient boosting, and model evaluation. These algorithms dominate production tabular ML in 2026.
Deep Learning (PyTorch)
6–8 weeksBuild real intuition for how neural networks learn — from scratch training loops to transformers. PyTorch is the 2026 production and research standard.
MLOps & Experimentation
3–4 weeksTransform ad-hoc model training into reproducible, versioned, production-ready engineering — experiment tracking, pipeline automation, and model governance.
Cloud & Model Serving
4–5 weeksFrom trained model to production endpoint — Docker, FastAPI, Kubernetes, and managed cloud ML platforms for scalable, monitored inference.
LLM Fine-Tuning & RAG
4–5 weeksAdapt foundation models to domain-specific tasks with LoRA/QLoRA, build production RAG pipelines, and orchestrate multi-step LLM workflows with LangGraph.
Inference Optimisation & Scale
3–4 weeksThe skills that separate ML engineers who can deploy prototypes from those who can run production AI economically — quantisation, distributed training, and drift monitoring.
Portfolio & Career
2–3 weeksBuild the portfolio that gets you hired — production-thinking projects, ML system design interview prep, and the GitHub profile that makes recruiters reach out.
Ready to start your path?
Python and math foundations appear in 95%+ of ML engineer job postings — start with the fundamentals.