Why This Role Stands Out
This hybrid role offers a fantastic opportunity to build and validate an AI-enabled data platform, with potential for the project to evolve into a broader product, providing significant career growth. You'll thrive here if you're a hands-on engineer eager to develop backend services and AI agents using cutting-edge frameworks, so don't miss out on applying!
Quick Overview
Job Description
Nextdata is looking for an experienced AI Platform Engineer for an initial three-month project focused on building and validating an AI-enabled data platform capability.
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.
If successful, the project may continue and develop into a broader Nextdata product.
What You’ll Do
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.
What We’re Looking For
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.
Contract Scope
Initial term: three to four months.
Focus: hands-on implementation and delivery.
No customer-facing responsibilities.
Potential extension if the project is successful.
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