What a AI/ML Engineer does

AI/ML engineers turn models into working software: they train and fine-tune models, build LLM features such as retrieval and agents, and run all of it reliably with containers, pipelines and monitoring.

This roadmap is built from 50 job descriptions posted in the last 90 days. It starts from Python and classical machine learning, adds deep learning and language models, and then the engineering that employers ask for: serving, evaluation, containers, Kubernetes and monitoring. The Claude Certified Architect, Foundations certification is part of this roadmap.

At about 12 hours a week, most people reach Job-ready in 10 to 15 months, faster if you already work as a software engineer or data scientist.

We read 50 AI/ML Engineer and Machine Learning Engineer job descriptions posted between July and September 2026: 32 in India and 18 global remote roles.

• Machine learning and Python are the entry ticket. Machine learning appears in every posting and Python in 88%.

• Deep learning is a Must-have. 68% name deep learning, 72% PyTorch and 64% TensorFlow.

• LLM work is now standard. LLMs and generative AI appear in 62% of postings, NLP in 48%, retrieval-augmented generation and AI agents in 30% each, fine-tuning in 28% and LLM evaluation in 20%.

• Production matters as much as modelling. MLOps appears in 58% of postings; Docker in 36%, Kubernetes and CI/CD in 32% each, monitoring in 24%.

• Data skills are still expected. Statistics in 42%, Spark in 40%, SQL and data pipelines in 36% each.

• Cloud is common. AWS in 40%, Azure 32% and Google Cloud 28%.

• People skills show up too. Stakeholder management in 44% and communication in 36%, rising to 46% of mid and senior postings.

Method, full frequency tables and every job link are in the research file linked from this page.

Popular AI courses stop at a notebook or a chatbot demo. The job descriptions ask for systems that work next month. This roadmap adds:

• Evaluation first: golden sets, graders, calibrated LLM judges and CI gates for prompt and model changes.

• Retrieval measured, not assumed: recall at k, hybrid search and refusals before answer quality.

• Agents with guardrails: narrow tools, budgets, approvals and task suites instead of open-ended loops.

• Decisions with numbers: when a TF-IDF or embedding baseline beats an LLM, and when fine-tuning is worth it.

• Operations: containers, Kubernetes, autoscaling, safe rollouts and model monitoring.