Artificial Intelligence Developer - RAG/LLM Models

Job Description :

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.