Architect

Key ResponsibilitiesDesign 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. SkillsStrong 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 EmbeddingsStrong 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.