What You'll Be Doing
Architecting and building multi-agent AI systems end-to-end, combining frameworks such as LangGraph or Haystack where they add value with bespoke orchestration, state management and tool-calling logic where a framework won't cut it
Writing production-grade code with reliability, latency and graceful failure handling designed in from the start rather than bolted on afterwards
Embedding LLMs and vision-language models into live reasoning, search, summarisation and task-execution workflows, backed by solid retrieval, memory and guardrail design
Deploying across cloud, on-premises and fully offline/air-gapped settings, adapting your approach to each environment's security and connectivity constraints
Taking end-to-end ownership of production pipelines, from raw data ingestion through to live inference
Building the monitoring, evaluation and guardrail tooling that shows how a system is actually performing in the field
Setting technical standards for agentic AI within the team, establishing the patterns other engineers follow and leaving clear documentation behind
What We're Looking For
Robust software engineering, including developing LLM-powered or agentic systems, and a track record operating at senior/lead level
Strong engineering fundamentals - code that's clean, tested and built to last, not just prototypes that work once
Commercial track record building and owning multi-agent or agentic AI systems in live production settings, as an engineer rather than an integrator
Strong Python skills, comfortable working with LangGraph, LangChain, Haystack or similar, and equally comfortable dropping below the framework when that's the right call
Real-world experience deploying AI/ML systems into production, beyond notebooks and proof-of-concepts
Confidence across inference performance, data pipeline design, state/memory handling, and tool or function calling
Experience building retrieval and search over very large, multi-modal datasets (multi-terabyte scale, spanning text, imagery, telemetry and sensor feeds), including indexing and embedding at scale
Comfortable with Docker, Git and cloud infrastructure, ideally AWS
Understanding of secure deployment approaches: air-gapped, on-premises or sovereign cloud
Nice to Have
- Multimodal reasoning and/or edge or offline AI deployment experience
- Kubernetes experience (EKS or OpenShift)
- MLOps background: evaluation, monitoring, reproducibility
- Observability for agentic systems, covering drift, agent behaviour and performance
- Familiarity with agent-to-agent orchestration protocols or the Model Context Protocol (MCP)
- Awareness of secure-by-design frameworks (ISO 27001, NIST, OWASP)
- Background in defence, national security or other regulated sectors
- Open-source AI/ML contributions
Why Apply
Real technical ownership: shape the architecture, not just work through a backlog
Work at the leading edge of agentic and generative AI, on systems people genuinely depend on
Join a fast-scaling business with a strong chance of the contract extending or evolving further
Flexible, hybrid working arrangement
Strong day rate reflecting seniority and scope