Artificial Intelligence Engineer

📍 Liverpool | Hybrid: 2-3 days on-site + client travel

This is not a theoretical or demo-driven AI role. We’re hiring engineers who design, deploy, and scale agentic AI systems in production. You’ll partner directly with enterprise and public-sector clients, taking initiatives from early proof-of-concept through to mission-critical platforms used by hundreds or thousands of users.

If you enjoy operating at the intersection of deep engineering, real-world delivery, and client impact, this role was built for you.

• Architect agentic AI systems – Design and own full agent lifecycles (plan → act → reflect), orchestration layers (DAGs, state machines), and memory strategies.
• Build production-grade tooling – Implement schemas, safety controls, retries, rate limiting, and extensible SDKs.
• Develop memory & retrieval services – Create episodic and semantic memory pipelines, RAG APIs, deduplication, and summarisation agents.
• Deploy at scale – Productionise systems using Kubernetes, serverless, GPU infrastructure, CI/CD pipelines, observability, and auto-scaling.
• Ensure reliability & governance – Apply guardrails, cost/latency controls, checkpointing, and critic/reviewer patterns.
• Evaluate and monitor – Build automated evaluation frameworks, golden datasets, retriever testing, and drift detection.
• Work with clients – Lead technical workshops, delivery roadmaps, and AI maturity assessments, translating complexity into outcomes.

• Hands-on experience shipping LLM or agentic AI systems into production.
• Strong software engineering fundamentals across orchestration, deployment, monitoring, and reliability.
• Experience with agentic architectures (e.g. LangGraph, ReAct, CoT loops) or building custom frameworks from first principles.
• Degree in Computer Science, AI, or related discipline.
• Experience with PyTorch or TensorFlow, vector databases, RAG pipelines, and orchestration platforms.
• Comfortable operating in client-facing delivery environments.

• Background in consulting, scale-ups, or high-ownership start-up environments.
• Exposure to regulated or high-stakes industries.
• Self-directed, entrepreneurial mindset with a bias toward execution.

• Real Impact – Deliver production-ready agentic AI systems solving real operational problems.
• Technical Depth – Work beyond “glue code” on orchestration, deployment, and system reliability.
• Global Exposure – Hybrid role with international client and team collaboration.
• Career Acceleration – Influence how enterprises adopt and scale agentic AI.
• Culture – Fast-moving, delivery-focused, and genuinely at the frontier of applied AI.

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