Agentic Systems Engineer

Overview

We're looking for an Agentic Systems Engineer, to work on systems that haven't existed before. Think of an Agentic Coach that navigates the unpredictability of human biology, or the software architecture behind a fully automated Dark Lab. These aren't chatbots waiting for questions. They're proactive systems that maintain world models, interpret human intent, and intervene before the user knows to ask. These systems create genuine impact for clients tackling problems no one else can solve.

Building them means designing and wiring agent harnesses: layered architectures that span from sensor-edge reasoning to high-level planning, backed by stateful multi-turn reasoning, persistent per-user memory, and robust evaluation pipelines.

You'll work in two directions at once. Internally, you'll use the latest AI tools to sharpen how we build, prototype, and ship. Externally, you'll deliver the breakthrough systems our clients deploy, designed to move from prototype to production at pace.

There's no single technology stack. You'll work with agents, multi-agent systems, and the latest generative AI tools, but your work will always be grounded in solid software engineering. Expect to build scalable distributed systems in core languages like Python and TypeScript, leveraging frameworks such as NextJS and Pydantic, agent orchestration tools (LangGraph, CrewAI), and protocols like MCP. You'll deploy on cloud platforms (AWS, GCP, Azure) using Docker, Kubernetes, and infrastructure-as-code, backed by rigorous CI/CD and observability (OpenTelemetry, Prometheus).

Due to the clients we work with, you must be eligible for UK security clearance.

What you'll be doing

• AI System Architecture: Designing and implementing the logic for autonomous systems, including maintaining world models, integrating multimodal sensing, and building real-time human-machine interaction loops.
• Infrastructure & Production Engineering: Designing the underlying software for scalable, secure, and observable cloud deployments. You will apply the engineering rigor required to take experimental AI from concept to robust live launch.
• Rapid Client Prototyping: Driving a demo-first delivery model. You will focus on fast experimentation to actively show tangible value and working software to clients, rather than describing it in slide decks.
• Domain Translation & Integration: Partnering directly with deep-tech subject matter experts (like biologists and roboticists) to apply software solutions to complex physical constraints.

What you need to succeed:

• Applied AI Expertise: You have hands-on experience utilising multi-agent frameworks, long-term memory systems, RAG pipelines, vector/graph databases, and reasoning-loop patterns (such as ReAct and Chain-of-Thought).