Design, build, and maintain scalable ETL pipelines and data models for analytics and ML. Ensure data quality, optimize performance, enforce governance and security, and collaborate with stakeholders to deliver reliable, production-ready data for reporting and data science.

Lead design, development, and deployment of production-ready ML/AI systems using Python, PyTorch, LLMs and related tools. Drive model research, tuning, monitoring, cross-functional integration, and mentor junior engineers while prioritizing practical business impact.

Collaborate with development and QA to implement DevOps best practices: containerization (Docker), IaC, and CI/CD pipelines. Assist managing scalable cloud-native infrastructure (pref. AWS), enhance monitoring/logging, automate deployments, and improve release velocity through scripting and configuration management.