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# Agentic Systems Engineer (Digital Services)

- **Company:** Cambridge Consultants Ltd
- **Location:** Cambridge, UK
- **Salary:** £81,315.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Engineering, Computer Programming, Continuous Integration, DevOps, Distributed Systems, Graph Database, Python (Programming Language), Software Architecture, Prometheus, Next.js, Software Engineering, TypeScript, Google Cloud, Chatbots, ReactJS, Multi-Agent Systems, SC Clearance, Containerization, Kubernetes, Docker
- **Published:** August 2, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5824418395

## About the Role

We hire self-motivated builders, not ticket-takers. If you thrive in highly agile environments, can cut through ambiguity with first-principles thinking, and want to ship things that matter, this is the kind of place where you'll feel at home., 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).

Core Software Fluency: You possess strong programming skills in Python and TypeScript, coupled with practical experience in cloud engineering, containerisation, and modern DevOps. You know that great agents still require rock-solid software foundations.

High Tolerance for Ambiguity: You are energised by uncertainty and shifting targets. You treat changing requirements as a normal part of frontier work, and you know exactly when to ship a rough prototype to learn quickly versus when to slow down for architectural rigour.

Cross-Disciplinary Communication: You can seamlessly navigate the gap between code and reality, collaborating with clients, roboticists, and scientists to clearly deliver the "art of the possible."

## Description

You'll be working 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.

The examples below describe the core domains our AI Engineers own within the Digital Services team. We don't expect every candidate to have mastered all of these, but if you can demonstrate a strong combination of the following, we'd love to hear from you.

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.


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