Principal AI Platform Engineer
San Francisco, CA - Hybrid
Job Description
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.
Ability to build production systems that are safe, observable, testable, and reliable.
Strong Python skills and comfort working across backend services, data systems, APIs, and AI frameworks.
Product-minded judgment: you know the difference between a demo, a customer-specific workaround, and a reusable platform capability.
Comfort working in ambiguous areas where the patterns are still being defined.
Nice to have
Experience with data mesh, data products, semantic models, catalogs, governance platforms, or data marketplaces.
Experience with MCP servers, tool registries, LLM orchestration, RAG systems, or multi-step agents.
Experience with Databricks, Snowflake, BigQuery, Spark, DuckDB, Postgres, graph databases, vector databases, or lakehouse architectures.
Experience with enterprise identity and authorization systems such as SSO, OAuth, OIDC, SAML, SCIM, RBAC, ABAC, or policy engines.
Experience evaluating AI systems for retrieval quality, tool-use accuracy, groundedness, reproducibility, and failure modes.
Never miss an opportunity! Create an alert based on the job you applied for.