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Build and operate governed MLOps and LLMOps platforms on Databricks. Productionize ML and GenAI models, RAG pipelines, vector search, and inference services; implement CI/CD, data quality, security, lineage, observability, incident response, disaster recovery, and cost controls. Partner with data science, engineering, platform, product, and security teams to ensure reliable, secure, compliant, and cost-effective AI workloads across development, QA, and production environments.

Own and operate AWS, Azure, and Databricks infrastructure supporting data engineering, data science, and MLOps teams. Responsibilities include cloud provisioning, networking, IAM, Unity Catalog administration, security controls, infrastructure as code, platform onboarding, automation, cost management, compliance evidence, troubleshooting, and operational documentation. The role establishes secure DEV, QA, and PROD environments and serves as the primary escalation point for Databricks and cloud platform issues.

Build and maintain batch and streaming data pipelines across AWS, Azure, and GCP using Databricks, Spark, PySpark, SQL, and Delta Lake. Implement medallion architecture, CDC, SCD, data quality, governance, security, metadata, and lineage controls. Configure storage, orchestration, CI/CD, monitoring, troubleshooting, and performance optimization. Collaborate with architects, data scientists, engineers, analysts, and product teams while documenting pipelines, transformations, testing, and operational procedures.

Build governed Databricks Lakehouse foundations and productionize machine learning and GenAI solutions, including forecasting, anomaly detection, NLP, RAG, and predictive analytics. Develop reliable batch and streaming workflows, CI/CD, monitoring, governance, security, lineage, and semantic models. Collaborate with product, data, platform, and domain teams to deliver measurable customer value while meeting reliability, compliance, cost, and operational objectives.