Senior AI Engineer

Role Overview

- Lead hands-on engineering across LLMs, RAG, Graph RAG, data pipelines to deliver trusted GenAI capabilities on Azure / AWS / Internal platform.

- Design, prototype, and scale AI products that improve decision-making, efficiency, and client experience in UK financial services

- Build Agentic AI capabilities (Tools, MCP, function calling, lang graph, Microsoft agent framework)

Key Responsibilities

GenAI, RAG & Graph RAG Engineering

- Engineer RAG pipelines (chunking, embeddings, hybrid search, re-ranking, context assembly).

- Build Graph RAG by combining vector retrieval with knowledge-graph traversal for provenance and multi-hop reasoning.

- Implement graph-aware retrieval (subgraph construction, path finding, query planning, hybrid ranking).

- Deliver LLM applications (Q&A, summarisation, insights, workflow automation, risk/compliance intelligence).

- Prompt engineering and advanced prompt techniques

Innovation Prototyping & Experimentation

- Build rapid PoVs and measure uplift vs. baselines (quality, latency, cost, safety).

- Test agentic patterns (tool-use, planning, self-critique) guided by graph retrieval.

Engineering & Deployment Excellence

Ship clean, testable Python

- Operate at scale on Azure / AWS / Internal platform with Docker/K8s and modern API gateways.

Know-how of Continuous Integration/Continuous Deployment, Infra as Code, Test Automation and observability (logs/metrics/traces).

Governance, Risk & Responsible AI

Embed controls for hallucination mitigation, retrieval accuracy, PII protection, and model risk.

Monitor metrics (graph coverage, EL precision/recall, path correctness, groundedness, latency, cost/query).

Leadership & Collaboration

- Mentor engineers, codify best practices, and contribute to reference architectures.

Required Skills & Experience

- Python (LangChain, LlamaIndex) and SQL (analytics, optimisation).

- RAG & Graph RAG design/optimisation; vector DBs (Pinecone, FAISS, Milvus, Chroma).

- Knowledge graphs/graph DBs (Neo4j)

- Production services on Azure / AWS / Internal platform; CI/CD, Docker/K8s, testing frameworks, observability; MLflow or equivalent.

Azure/AWS certifications (AI Engineer, Data Engineer) or equivalent cloud credentials.

Agentic AI capabilities (Tools, MCP, function calling, lang graph, Microsoft agent framework)

Education

Degree in Computer Science (or related: ML, Data Science, Mathematics, Engineering) highly advantageous.

Nice to have

AI/ML engineering or advanced data science within regulated environments.

Proven delivery of agentic workflows (tool-use, planning, self-verification) in production.

Multi stack including java script, React, Nodjes experience is valued