Platform Engineer
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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.
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