What we are looking for

Senior AI Full-Stack Engineer

Company: 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 positive enough.

What you will build and own

Agentic 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.

Enterprise integration. Integrate with Microsoft Graph, SharePoint, Salesforce and client identity providers using OAuth 2.0 / OIDC, including delegated and On-Behalf-Of flows so agents act with the user's permissions, not a service account's. Respect the client's existing RBAC and data boundaries in everything you build.

Multi-tenant product engineering. Build features that are tenant-isolated by construction: row-level security or schema-per-tenant where appropriate, tenant-scoped credentials and vector indexes, per-tenant cost attribution, audit logging that a client's security team will accept.

Front-end. Build the interfaces through which users work with agents chat and task UIs, streaming responses, citations and provenance, review-and-approve flows, dashboards and admin consoles in React/Next.js and TypeScript, to a standard you would put in front of a client executive.

Quality and evaluation. Write the tests. Build eval sets for every LLM-driven feature and run them on every change. Instrument agents with tracing so that when a client asks "why did it say that", you can answer.

What we are looking for

Must have

- 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 wrong: hallucinations, latency, token cost, prompt regressions, retrieval misses, and how you measured and fixed them. • Strong Python backend engineering: FastAPI or similar async frameworks, typed code (Pydantic, mypy), background workers, and API design you would be happy to hand to another team.
- 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-only tokens.
- 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.

Strongly preferred

- Hands-on with agent frameworks and protocols: MCP servers, Claude Agent SDK, LangGraph or comparable; durable workflow engines such as Temporal.
- Experience with sandboxed code execution for LLM-driven data analysis, or with graph databases and ontology-driven data models.
- Document processing at scale: OCR, layout-aware parsing, table extraction, chunking strategies.
- Observability for LLM applications: tracing platforms such as LangFuse or LangSmith, eval frameworks, cost dashboards.
- Working knowledge of containers, Kubernetes and CI/CD (GitHub Actions or Azure DevOps) enough to own your service's deployment, not to run the cluster.
- Experience using AI coding agents (Claude Code, Cursor or similar) as a serious part of your workflow.

How we work

- Remote, India-based. Occasional travel to Delhi NCR for team days.
- Working hours. Standard India working hours. Our clients are in the UK, Europe and the Middle East, so you will occasionally take a late-afternoon or evening call to cover overlap this is the exception, not the routine.
- Ownership. You own what you ship. When a feature you built misbehaves in production, you lead the fix and the explanation.
- Joining. We are hiring for an immediate need and strongly prefer candidates who can join right away or are already serving notice.

Why this role You will build agentic systems that enterprises actually run their work on not demos across the full stack, from ingestion and retrieval through agent orchestration to the interface a client's team uses every day. You will work alongside engineering leadership that builds hands-on and knows exactly what good looks like, on a platform whose scope is growing faster than we can hire for it.

📌 Senior AI Full-Stack Engineer (Delhi)
🏢 Grey Chain
📍 Delhi