10+ years of ML/MLOps/Data Science engineering experience, strong Azure/Databricks expertise,
Lead the architecture, development, and evolution of an enterprise ML platform on Databricks.
Design and implement end-to-end MLOps pipelines covering model development, testing, deployment, monitoring, retraining, and rollback.
Build and manage enterprise feature stores, including feature engineering, governance, versioning, lineage, quality, and reuse.
Develop scalable batch and real-time ML inference solutions, APIs, and production integration patterns.
Implement MLflow, Unity Catalog, Delta Lake, Databricks Workflows/Jobs, Model Registry, and CI/CD/CT pipelines.
Establish best practices for ML governance, security, reproducibility, observability, monitoring, drift detection, and data quality.
Partner with Data Scientists and Data Engineers to productionize and operationalize machine learning models.
Use Python, SQL, software engineering, and system-design skills to build scalable and maintainable ML solutions.
Collaborate with Cloud, DevOps, Security, Architecture, Risk, Governance, and business teams, while mentoring engineers and reviewing technical designs.