Principal AI Platform Engineer
SWITS DIGITAL Private Limited
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.
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.
Vacancy posted 5 days ago
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