Principal AI Platform Engineer

📋 Description

• Design agentic data interfaces for AI agents to safely use data.
• Build MCP-compatible endpoints, tools, and APIs for governed access.
• Develop data agents that reason over metadata, semantics, contracts, policies.
• Make data products AI-ready across SQL, documents, vectors, graphs, APIs.
• Build safe query and action flows with access checks and audit trails.
• Partner with product, engineering, and customer teams to turn use cases into capabilities.

🎯 Requirements

• Strong experience in data engineering, data platforms, distributed systems, or enterprise data infrastructure.
• Practical experience building AI-enabled data systems, retrieval systems, semantic layers, or data agents.
• Strong knowledge of SQL, APIs, documents, vector search, knowledge graphs, and metadata systems.
• Experience with agentic interfaces, tool-calling, MCP or similar protocols, function calling, or AI backends.
• Good understanding of governance: access control, policies, contracts, lineage, data quality, PII protection, and auditability.
• Strong Python skills and comfort across backend services, data systems, APIs, and AI frameworks.

🎁 Benefits

• Data mesh, data products, semantic models, catalogs, governance platforms.
• MCP servers, tool registries, LLM orchestration, RAG systems.
• Databricks, Snowflake, BigQuery, Spark, DuckDB, Postgres, graph/vector DBs.
• Enterprise identity and authorization systems (SSO/OAuth/OIDC/SAML).
• Evaluating AI systems for retrieval quality, grounding, and reproducibility.