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Senior Machine Learning Engineer
About the role : Own end‑to‑end machine learning for structured, time‑aligned industrial datasets, from data pipelines to production deployments. Deliver reliable, calibrated ML services with strong observability and rollback.
Responsibilities
• Design and maintain robust ETL/feature pipelines for large structured datasets.
• Train and evaluate classification/regression models; implement calibration and uncertainty.
• Build production ML services with CI/CD, versioned artifacts, monitoring, and drift detection.
• Develop secure, low‑latency deployments in on‑prem/VPC environments using containers.
• Create documentation, diagnostics dashboards, and automated alerts/rollback.
Minimum qualifications
• 4+ years shipping ML systems on tabular data in production.
• Strong Python, SQL, and experience with scikit‑learn/XGBoost/LightGBM or tabular DL.
• Proven CI/CD, containerization, and observability skills for ML services.
• Familiarity with calibration (e.g., ECE), PR/ROC evaluation, and imbalanced data handling.
• Experience with secure deployments (access control, secrets, audit logging).
Nice to have
• Background with industrial/manufacturing or time‑series/quality datasets.
• Experience with experiment design, labeling strategies, and slice‑wise diagnostics.
• Cloud ML experience and migration to on‑prem deployments.
How to apply : Share a resume and a brief note describing one production ML system you shipped (data → deployment → monitoring), highlighting how you handled calibration, drift, and rollback.
Key Skills
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