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.

- Strong 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.