Machine Learning Engineer

Turn research models into scalable, production-ready systems that survive real traffic, real data drift and real audit.

• 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.

• 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.