Job Description
Key responsibilities
1) Model & Solution Engineering Translate business problems into ML formulations; select suitable architectures (., gradient boosting, transformers) with clear success metrics. Build end-to-end pipelines: feature extraction, training, hyperparameter tuning, and packaging models as reproducible artifacts. Optimize inference (quantization, distillation, mixed precision) for latency and throughput on CPU/GPU. Conduct evaluation beyond accuracy (calibration, fairness, cost-sensitive metrics, PR/ROC under imbalance).
2) MLOps, Deployment & Observability Implement model versioning, lineage, and experiment tracking; manage rollbacks and canary releases. Build real-time and batch inference services; integrate with message buses and vector databases. Monitor for schema checks, data drift, performance regression, and cost observability. Create alerting and autoscaling policies tied to SLAs, maintain incident runbooks for model services
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