ML Engineers sit between research and production. You’ll turn notebooks into services, manage features and datasets, evaluate models beyond accuracy (bias, drift, latency, cost), and wire up monitoring to keep performance stable in the wild. The work blends software engineering, data science, and DevOps. It suits analytical builders who want measurable impact.
Common entry paths: software or data engineering → ML platform/serving- data science → applied ML with stronger engineering. \n\nAI is booming, but deployment is the hard part. Foundation models make prototyping easier- reliability, safety, and economics separate pilots from products. Expect growing emphasis on evaluation harnesses, human-in-the-loop design, prompt/feature stores, and governance.
Practical skills: Python, containers, APIs, CI/CD, experiment tracking, vector databases/RAG, and cost/latency tuning. The next decade favors engineers who can translate model capability into user-visible outcomes-choosing simpler, cheaper solutions when they work-and who document assumptions and guardrails clearly. If you like owning results, not just metrics, ML engineering is a high-leverage path.