AI Engineer

You will work on the design and development of AI systems where multiple specialised agents collaborate, reason, use tools and interact with external systems to complete complex workflows.

This is a hands-on engineering position covering agent architecture, LLM orchestration, retrieval, tool use, evaluation and production deployment., * Design and build production-grade multi-agent and agentic AI systems.

• Develop agent orchestration, routing, planning and tool-use workflows.
• Build integrations between AI agents, APIs, databases and internal/external tools.
• Develop RAG and retrieval systems using vector databases and modern retrieval techniques.
• Implement function calling, structured outputs and context management.
• Build evaluation frameworks to measure agent performance, accuracy and reliability.
• Develop guardrails and validation layers to reduce hallucinations and improve system safety.
• Deploy and operate AI services in production cloud environments.
• Work closely with product and engineering teams to take AI products from concept through to production.

They are looking for an AI Engineer with strong software engineering fundamentals who has hands-on experience designing, building and deploying agentic AI systems into production., * Strong Python and software engineering fundamentals.

• Commercial experience building LLM-powered applications.
• Hands-on experience with multi-agent or agentic AI systems.
• Experience with frameworks such as LangGraph, LangChain, CrewAI, Semantic Kernel, Google ADK or similar.
• Strong understanding of RAG, vector search, embeddings and retrieval architectures.
• Experience with LLM tool/function calling and API integrations.
• Experience deploying AI/ML applications into production.
• Understanding of LLM evaluation, observability and monitoring.
• Experience with AWS, Azure or GCP.
• Docker, Kubernetes and modern CI/CD experience is advantageous.

Ideal Background

You have built AI agents that do more than answer questions.

You have experience creating systems where agents can reason across multiple steps, call tools, retrieve information, interact with other agents and execute workflows reliably in a production environment.

Experience building customer-facing AI products or AI systems operating at meaningful production scale would be particularly relevant.