Principal AI Platform Engineer
The Principal AI Platform Engineer at Nextdata designs and builds the interfaces, systems, and agents that make governed enterprise data usable by both humans and AI agents.
The role
The Principal AI Platform Engineer at Nextdata designs and builds the interfaces, systems, and agents that make governed enterprise data usable by both humans and AI agents.
This role sits at the intersection of data engineering, AI engineering, distributed systems, and product architecture. You will help define how autonomous data products expose their semantics, contracts, policies, metadata, and outputs to AI systems through agentic interfaces such as MCP-compatible endpoints, typed APIs, semantic tools, and data agents.
You will not just build pipelines for AI models. You will build the product capabilities that allow AI systems to discover the right data, understand its meaning, request access, execute safe actions, and return reliable answers with context, lineage, and policy enforcement.
Design agentic data interfaces that let AI agents discover, understand, and safely use data products.
Build MCP-compatible endpoints, tools, and APIs for governed AI/data access.
Develop data agents that reason over metadata, semantics, contracts, policies, and data outputs.
Make data products AI-ready across SQL, documents, vectors, graphs, APIs, and semantic models.
Build safe query and action flows with access checks, policy enforcement, approvals, and audit trails.
Work on retrieval, semantic search, tool selection, context construction, and answer grounding.
Define reusable patterns for agent-readable metadata, structured outputs, observability, and evaluation.
Partner with product, engineering, and customer teams to turn enterprise AI/data use cases into product capabilities.
You are the right fit if you have
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.
The role
The Principal AI Platform Engineer at Nextdata designs and builds the interfaces, systems, and agents that make governed enterprise data usable by both humans and AI agents.
This role sits at the intersection of data engineering, AI engineering, distributed systems, and product architecture. You will help define how autonomous data products expose their semantics, contracts, policies, metadata, and outputs to AI systems through agentic interfaces such as MCP-compatible endpoints, typed APIs, semantic tools, and data agents.
You will not just build pipelines for AI models. You will build the product capabilities that allow AI systems to discover the right data, understand its meaning, request access, execute safe actions, and return reliable answers with context, lineage, and policy enforcement.
Design agentic data interfaces that let AI agents discover, understand, and safely use data products.
Build MCP-compatible endpoints, tools, and APIs for governed AI/data access.
Develop data agents that reason over metadata, semantics, contracts, policies, and data outputs.
Make data products AI-ready across SQL, documents, vectors, graphs, APIs, and semantic models.
Build safe query and action flows with access checks, policy enforcement, approvals, and audit trails.
Work on retrieval, semantic search, tool selection, context construction, and answer grounding.
Define reusable patterns for agent-readable metadata, structured outputs, observability, and evaluation.
Partner with product, engineering, and customer teams to turn enterprise AI/data use cases into product capabilities.
You are the right fit if you have
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.