Job Summary

Full ML model lifecycle: dataset curation, feature engineering, model training, evaluation, optimization, continuous deployment, and monitoring. NLP, computer vision, time series, anomaly detection, recommender systems. End-to-end ML pipelines (Kubeflow, SageMaker, Vertex AI). Advanced MLOps with CI/CD/CT. Model governance, registry management, drift detection. Domain-specific model optimization for insurance (fraud, claims, underwriting). ML platform architecture and responsible AI controls. Junior: model training (scikit-learn, XGBoost, PyTorch/TF), basic MLOps, API development for model serving. Senior: end-to end pipelines, advanced MLOps, governance, insurance-domain optimization. Expert: ML platform architecture, enterprise ML strategy, custom training infrastructure, model audit.

Key Responsibilities

1. To design and architect large-scale solutions, ensuring scalability, performance, and security.
2. To train and develop team so as to ensure that there is an adequate supply of trained manpower in the said technology and delivery risks are mitigated.
3. To continuously upskill with cutting-edge tech to deliver high-quality, future-proof solutions meeting client expectations and industry standards.
4. To leverage domain/tech expertise to gather client needs, deliver solutions, and craft a technology strategy aligned with business goals.

Skill Requirements

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Other Requirements

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