Key Responsibilities
- Design and implement Databricks architecture on Azure and/or GCP (workspace, clusters, networking, security).
- Lead platform setup, governance, and optimization (Unity Catalog, cost control, access management).
- Build and deploy AI/ML solutions using Databricks (MLflow, Feature Store, Model Serving).
- Develop GenAI / LLM solutions (RAG pipelines, embeddings, vector search).
- Integrate LLMs (Azure OpenAI / Vertex AI / other APIs) within Databricks.
- Deliver scalable Lakehouse architecture solutions.
- Collaborate with engineering and business teams for end-to-end AI use cases.
- Ensure performance tuning, reliability, and security of workloads.
Skills
Strong hands-on experience in Databricks platform setup and architecture (mandatory).
Expertise in the Databricks AI Stack: MLflow AutoML Feature Store Model Serving Vector Search RAG Architectures Embeddings
Strong experience with PySpark, Delta Lake, and Spark performance optimization. Experience with cloud ecosystems:
Azure Databricks and related Azure services GCP Databricks, BigQuery, GCS, and Vertex AI Hands-on experience integrating LLMs and Generative AI solutions.
Knowledge of Unity Catalog, governance, data security, and access management.
