Senior AI Full-Stack Engineer
- Compensation
- Not disclosed
- Location
- Delhi / NCR
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Full time
- Experience
- 5+ years
- Category
- Fullstack
- Posted
Skills
Must have
- Senior AI Full-Stack Engineer
- Company: Grey Chain AI
- Location: Remote
- Experience: 8+ Years
- Employment Type: Full Time
- 5-8 years building production web applications, with the last 2+ spent as a full-stack engineer owning features from database to UI.
- You have shipped LLM features to real users RAG, tool-calling, structured extraction or agent workflows using Claude, OpenAI, Bedrock or Azure OpenAI APIs, and you can talk concretely about what went
- Strong React/Next.js and TypeScript you build the UI well, not merely tolerate it. Streaming, optimistic updates, complex forms and accessible components should all be familiar.
- Deep PostgreSQL: schema design, indexing and query performance, migrations, transactions, and experience with pgvector or a comparable vector store.
- Enterprise integration in production: at least one of Microsoft Graph / SharePoint, Salesforce, or OAuth 2.0 / OIDC SSO against Entra ID or Okta including the delegated-permission flows, not just app-
- Multi-tenant SaaS experience: tenant isolation, RBAC, audit logging, and the discipline to keep data from one client from ever reaching another.
- You test your work and expect others to. Unit, integration and LLM evals are part of the definition of done, not a follow-up ticket.
- Clear written and spoken English; comfortable demoing to and taking questions from client stakeholders.
Full description
What we are looking for
Senior AI Full-Stack EngineerCompany: Grey Chain AI
Location: Remote
Experience: 8+ Years
Employment Type: Full Time
About the Role
We are growing our engineering teams and hiring senior full-stack engineers who have already shipped LLM-powered features to real users and want to do it at the level of a platform rather than a single app. You will work flexibly across two kinds of work, and most engineers here do both. Platform work: building the capabilities of our agent platform itself new agent types, tools and MCP servers, skills, the codeexecution sandbox, workflow and long-running task infrastructure, ingestion pipelines, and the graph, vector and ontology layers that agents reason over. Product work: building client-facing AI products on that platform, from data model to API to UI, integrated into the client's Microsoft 365, Salesforce or identity estate, and deployed into their own cloud environment. Both kinds of work share a shape: a Python backend, a React/Next.js front-end, PostgreSQL underneath, an LLM in the loop, and an enterprise client at the end of it who expects the result to be secure, multi-tenant, auditable and fast. You will own features end to end schema, service, agent behaviour, UI, tests, and the evals that prove the LLM part actually works. You will join engineering teams that already ship to production. We expect you to raise the bar on them: through the quality of your own work, your code reviews, and your willingness to say when something is not good enough.
What you will build and ownAgentic features, end to end. Design and build agent workflows that solve real enterprise tasks: retrieval over client documents and structured data, multi-step tool use, specialised agents that hand off to one another, long-running jobs with durable state, and human-in-the-loop checkpoints. Own the prompts, the tool definitions, the orchestration logic and the evals not just the API around them.
Tools, MCP servers and skills. Build the tools agents call MCP servers over client systems and internal services, skills that package repeatable capabilities, and sandboxed code execution for data analysis. Design them so they are safe to expose to an LLM: strict schemas, least-privilege credentials, idempotency, and clear failure modes.
Data layer. Design PostgreSQL schemas that hold up under multi-tenant load and years of client data. Build ingestion pipelines for documents, structured data and enterprise sources; own chunking, embedding, indexing and the vector, graph and ontology structures that make retrieval accurate. Write the SQL that analytics agents generate and know when they are generating it badly.