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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