• Building agentic solutions: agents that plan and execute multi-step tasks over long horizons, with context compaction and tool-call repair so they stay reliable and on-budget.

• Designing and expanding agent tool ecosystems: file/document operations, sandboxed code and bash execution, web search, and enterprise integrations (e.g. MS Graph) — with well-typed, testable schemas.

• Orchestrating multi-agent / sub-agent systems that spawn parallel sub-tasks, coordinate, and report results back to a lead agent.

• Building stateful LLM workflows (e.g. LangGraph) for domain pipelines such as research, summarisation and report generation.

• Engineering RAG and retrieval pipelines: chunking, embeddings, vector search (pgvector / Azure AI Search), hybrid and re-ranked retrieval, and grounding LLM outputs in trusted sources.

• Producing structured, reliable LLM outputs (function calling / Pydantic) and designing robust prompts and agent skills.

• Building evaluation harnesses and offline batch evals (including deep-research style runs) to measure accuracy, faithfulness, cost and latency — and using those signals to iterate.

• Instrumenting agents with observability and tracing (e.g. Langfuse, OpenTelemetry) and tracking token usage and cost in production.

• Routing across multiple models and providers via an enterprise gateway (e.g. LiteLLM) and tuning model selection for quality, latency and cost.

• Producing clean, maintainable, efficient code deployed at scale in the Azure cloud; scaffolding new projects, pairing with engineers, and reviewing pull requests.

• Contributing to team stand-ups and the broader software development lifecycle, and participating in firmwide data science, ML and AI forums.

• Coaching and mentoring junior data scientists and engineers.

What we’re looking for: Applicants must be able to demonstrate the following key capabilities. We do not expect every candidate to tick every box, strong fundamentals and a track record of shipping LLM-powered products matter most.

Core skills:

• Strong Python development experience, with hands-on use of modern LLM frameworks and SDKs (e.g. OpenAI / Anthropic SDKs, LiteLLM, LangGraph).

• Deep, practical knowledge of prompt engineering, LLM workflows and agentic patterns, tool/function calling, multi-step agents, and structured outputs.

• Experience building and optimising RAG pipelines, including evaluation, with industry-standard tooling.

• Working knowledge of vector databases such as pgVector and Azure AI Search, plus embeddings and hybrid/semantic search.

• Strong experience with SQL databases such as PostgreSQL (or equivalents).

• A scientific, evaluation-first mindset: selecting appropriate methods, applying algorithms at scale, and using the scientific method to derive robust, defensible conclusions about model and agent behaviour.

• Strong critical thinking, analytical rigour and outstanding attention to detail.

• Proper source code management and confident use of Git.

• Good written and verbal communication, and the ability to work effectively with remote teams.

• A proactive, problem-solving approach and the ability to solve complex problems as part of a team.

Desirable skills:

• Experience with LLM observability and cost/quality tracing (e.g. Langfuse, OpenTelemetry).

• Experience routing across multiple models/providers (e.g. via LiteLLM or an enterprise gateway) and reasoning about model selection trade-offs.

• Experience designing multi-agent / sub-agent systems and agent tool ecosystems (web search, file/document tools, enterprise integrations such as MS Graph).

• Experience with computer-use / browser-automation agents (e.g. Playwright).

• Knowledge of classical ML (regression/boosting) and deep learning (CNN/RNN), preferably in NLP or CV.

• A research background in ML/LLM model development, and the ability to identify emerging techniques and apply them to practical situations.

• Experience with microservices, containerisation (Docker), and building/operating data pipelines at scale.

• Experience with message-queueing solutions (e.g. RabbitMQ, Kafka).

• Experience developing on cloud environments, particularly Azure (Azure OpenAI, AI Search, Blob, Key Vault, App Insights).

• Knowledge of agile software development lifecycles (SDLC) and experience on agile projects.

• TypeScript / full-stack experience is a strong bonus: much of our agent tooling and UI is built in TypeScript (Vercel AI SDK, NestJS, Next.js / React), and being able to work across the Python and TypeScript stack is highly valued.