AI Deployment Engineer

We’re looking for an experienced AI Deployment Engineer to join a highly capable technology and AI environment, working directly with business teams to turn emerging AI capabilities into measurable business outcomes.

This is not a traditional software engineering role. You will work end-to-end across discovery, workflow redesign, solution architecture, hands-on AI engineering, deployment and adoption. You will take ambiguous business problems and turn them into practical, production-ready AI solutions.

The role works across business, technology, data, cloud, cybersecurity, architecture, risk and compliance teams. Responsible for shaping and delivering AI-enabled solutions from initial concept through to production.

• Partner directly with business teams to identify opportunities where AI, automation or process redesign can deliver meaningful value.
• Design workflows across people, AI agents and enterprise systems, incorporating human oversight, controls, hand-offs and escalation paths.
• Design and implement AI patterns including agents, orchestration, RAG/retrieval, tool use, structured outputs and context engineering.
• Prototype, build, test, harden and deploy AI-enabled applications and intelligent workflows.
• Integrate models, APIs, data sources, workflow platforms and enterprise systems.
• Establish evaluation frameworks, regression testing, guardrails and observability to ensure solutions remain reliable and safe in production.
• Own solutions across the full lifecycle – from prototype and approval through deployment, monitoring, revalidation and eventual retirement.
• Work with stakeholders to measure adoption, productivity, cost, risk reduction and broader business value.

Experience as a technologist who combines strong hands-on AI engineering capability with commercial and business thinking.

• Experience delivering technology-enabled business change, including hands-on experience building AI-enabled solutions.
• Practical experience designing and shipping AI applications, agents or intelligent workflows.
• Strong understanding of agentic AI, orchestration, RAG, context engineering and AI application patterns.
• Experience working across data, APIs, SQL, cloud environments, CI/CD, integration, logging and production support.
• Strong development capability with Python and/or TypeScript.
• Experience with AI evaluation, testing, guardrails, observability and lifecycle management.
• An understanding of model behaviour and failure modes, with the ability to balance quality, latency, cost, reliability, risk and user experience.
• The ability to operate across strategy and architecture while remaining genuinely hands-on with delivery.