Machine Learning Engineer
Summary
Build and deploy production-grade ML systems that handle real traffic, data drift, and audits using cloud infrastructure and DevOps practices.
Turn research models into scalable, production-ready systems that survive real traffic, real data drift and real audit.
What you’ll do
- Take models from experiment to production service — packaging, serving, scaling and versioning them.
- Build the evaluation and monitoring that tells you a model has quietly stopped working.
- Engineer for the failure cases: latency budgets, fallback behaviour, and what happens when the model is wrong.
- Work inside enterprise constraints — security review, change control, data residency — without losing velocity.
- Review the work of others and raise the engineering standard around you.
What we’re looking for
- Strong software engineering fundamentals; you write code others can maintain.
- Experience deploying ML systems that real users depend on.
- Comfort with containers, CI/CD and cloud infrastructure (AWS preferred).
- Understanding of model evaluation beyond a single accuracy number.
- Experience in a regulated environment is valued.