Job Title

Key ResponsibilitiesLeadership & Strategy- Lead and mentor 200–250+ member data science, ML engineering, and analytics organization - Define enterprise AI/ML vision, roadmap, and best practices - Drive AI adoption across products, platforms, and business units - Partner with CXOs, product heads, and business stakeholdersHands-on AI / ML- Stay hands-on with model design, feature engineering, evaluation, and deployment - Build and review solutions involving: - Predictive & prescriptive analytics - NLP, Computer Vision, Deep Learning - MLOps and model lifecycle management - Ensure models are production-ready, scalable, and explainableDatabricks & Platform Engineering- Architect and implement AI/ML pipelines on Databricks (Spark, MLflow, Delta Lake) - Optimize large-scale data processing and model training - Collaborate with cloud and data engineering teamsMiddleware & Integration- Work closely with middleware teams to: - Integrate ML services via APIs / microservices - Enable real-time & batch model consumption - Ensure secure, scalable communication between applications, data platforms, and AI services - Understand middleware tools (Kafka, API Gateways, ESB, message queues, etc.)Healthcare / Insurance Focus- Apply AI to use cases such as: - Claims processing & fraud detection - Risk scoring & underwriting - Patient/member analytics - Operational optimization & complianceSoftware Development Practices- Embed AI into enterprise software development lifecycle (SDLC) - Promote Agile, CI/CD, DevOps, and MLOps practices - Ensure high code quality, documentation, and governance