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.
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.
Initial term: three to four months.
Focus: hands-on implementation and delivery.
No customer-facing responsibilities.
Potential extension if the project is successful.
Skills Required
- 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
- Experience with MCP servers, tool registries, or multi-step agents
- Experience with data products, data mesh, semantic models, catalogs, or governance platforms
- Experience with Databricks, Snowflake, BigQuery, Spark, DuckDB, Postgres, graph 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
What We Do
Nextdata OS turns legacy data pipelines and scattered tools into a unified layer of autonomous data products—AI-ready by design. By making data self-describing, self-governing, and instantly usable by agents and applications, Nextdata OS helps teams move faster, reduce risk, and deliver trusted data for AI, analytics, and innovation.
