Senior Machine Learning/AI Engineer

Translate ambiguous business problems into well-defined ML/DS problems. Investigate the real issue before proposing a solution, scope the right approach, and know when ML is the wrong answer.
Set technical direction for ML initiatives in your problem area. Make build-vs-buy, ML-vs-heuristic, and model-architecture decisions based on cost, latency, complexity, and business impact - not novelty.
Own ML systems end-to-end in production: data ingestion, training, serving, monitoring, retraining, and rollback. Anticipate and design around failure modes - data drift, train/serve skew, feedback loops, cold start, and label leakage - before they hit users.
Build and ship the services, APIs, and integrations around your models, working across the stack to deliver ML-powered features users actually touch.
Define the metrics that matter - offline evaluation, online A/B tests, and long-term business KPIs - and defend them. Question metrics you're handed when they don't reflect real user or business value.
Partner with product, design, and business stakeholders to shape the roadmap, not just execute it. Translate model behavior and data insights into narratives that drive decisions at both tactical and executive levels.
Raise the technical bar of the team through design reviews, code reviews, and mentorship. Your output is measured by what you ship and by what you unblock and elevate in others.