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Join a team building GenAI and agent-based applications for financial institutions from understanding complex systems to accelerating engineering delivery. Youll develop LLM-powered agents and retrieval systems that reason over large codebases and enterprise data to produce real engineering outcomes.

Roles and Responsibilities :

- Design, build and ship LLM-powered and agentic applications multi-step, tool-using, reliable.
- Build retrieval-augmented (RAG) systems over large, messy enterprise data source code, documentation, schemas.
- Work with vector databases and knowledge graphs for retrieval and reasoning.
- Engineer prompts, evaluations and guardrails; measure and systematically improve output quality.
- Integrate and route across multiple LLM providers; optimise for cost, latency, and on-prem / air-gapped constraints.
- Build backend services and automation that turn model output into auditable, production-grade artefacts.

Mindset and problem-solving :

- First-principles thinker decomposes ambiguous, open-ended problems and reasons from fundamentals rather than reaching for the nearest template.
- Inventive solutioner proposes novel approaches, challenges assumptions,



and weighs trade-offs to find the best answer, not just a working one.
- Bias to prototype experiments quickly, learns from what the models and data actually do, and iterates.
- Solid analytical ability and genuine curiosity; comfortable when the path isnt defined.

Must-have :

- Strong Python.
- Hands-on experience building LLM applications agents, tool use, RAG.
- Vector search (pgvector / FAISS / similar); knowledge graphs (Neo4j / Cypher) a plus.
- Prompt engineering plus systematic LLM evaluation.
- FastAPI / async services, containers (Docker / Podman), Git.

Nice-to-have :

- Familiarity with any Agent Development Kit / framework (e.g. Google ADK, OpenAI Agents SDK, LangGraph, CrewAI, AutoGen).
- Serena or similar semantic code-understanding / coding-agent toolkits (LSP- / MCP-based) for reasoning over and navigating large codebases.
- Model gateways (LiteLLM), MCP, orchestration libraries (LangChain / LlamaIndex).
- Code analysis / parsing / AST or static-analysis work.
- Delivery in regulated / on-prem / air-gapped environments; financial-services exposure.

📌 Artificial Intelligence Developer - RAG/LLM Models (India)
🏢 Upcruit
📍 India