Data Scientist → Applied AI Engineer

Why data scientists have a head start

You already understand models, data quality, statistics and experimentation — the hardest conceptual parts. The gap is usually software engineering: version control discipline, testing, packaging, APIs and production operations.

Skills that transfer directly

• → Evaluation and statistical thinking — directly applicable to LLM eval design
• → Data pipeline experience — retrieval pipelines are data pipelines
• → Python and the scientific stack

What you need to learn

• → Software engineering fundamentals: git workflows, testing, code review, packaging
• → APIs and services: building FastAPI services, async patterns, deployment
• → MLOps basics: containers, CI/CD, monitoring, model registries
• → LLM application patterns: RAG, tool calling, structured outputs, caching

Projects that get interviews

• → Take a personal analysis project and productionize it: API, tests, CI, deployment, monitoring
• → Build and evaluate an LLM pipeline on domain data you know well
• → Contribute to an open-source ML tooling project