AI Platform Engineer

This is a hands-on implementation role. You will quickly ramp up on an existing project and build backend services, agent tools, APIs, and data integrations. The role has no customer-facing responsibilities and less emphasis on architecture than a principal-level position., * Quickly understand the existing codebase, requirements, and technical direction.

• Build backend services, AI agents, tools, and APIs for governed data access.
• Implement MCP-compatible endpoints or similar agent interfaces.
• Integrate SQL, documents, APIs, metadata, semantic models, and vector search.
• Build retrieval, tool selection, context construction, and grounded response flows.
• Add access controls, policy enforcement, audit logging, tests, and observability.
• Deliver a reliable, documented system within the three-month project scope. *

• Strong experience with backend systems, data platforms, or distributed systems.
• Practical experience building agentic AI applications using LangChain, LangGraph, or similar frameworks.
• Experience with RAG, retrieval, semantic search, tool calling, and multi-step agent workflows.
• Strong Python and API development skills.
• Experience with SQL, documents, metadata systems, and vector search.
• Familiarity with MCP or similar agent interfaces.
• Understanding of access control, policies, lineage, data quality, PII protection, and auditability.
• Ability to become productive quickly in an unfamiliar codebase.
• Good judgment on scope, trade-offs, and production readiness.

Nice to Have

• Experience with data products, data mesh, semantic models, catalogs, or governance platforms.
• Experience with MCP servers, tool registries, or multi-step agents.
• Experience with Databricks, Snowflake, BigQuery, Spark, DuckDB, Postgres, graph databases, or vector databases.
• Familiarity with OAuth, OIDC, SAML, SSO, RBAC, ABAC, SCIM, or policy engines.
• Experience evaluating retrieval quality, tool accuracy, groundedness, and failure modes.