Artificial Intelligence Engineer

About the Company
The Senior AI Engineer (Agents & Automation) designs, builds, evaluates, and operates the AI agents, tools, and pipelines that put AI to work across the business, with digital platforms as the primary focus, and the whole enterprise as the canvas. This is a hands-on engineering role at the heart of our AI Centre of Excellence.
About the Role
You will ship production-grade AI agents and, just as importantly, build the enterprise-grade rollout machinery around them: a scalable, evaluated, versioned, and governed way of releasing agents, prompts, tools, MCP connectors, RAG, and data pipelines. You care as much about how reliably and repeatably we ship and operate AI as about the agents themselves.
Responsibilities
You will operate in an agent-led delivery model where AI agents are trusted to do meaningful work under human supervision with the emphasis on outcomes, quality controls, and repeatability rather than approving every step.You'll reengineer the process before you automate it, document what you build, and feed reusable patterns and guardrails back into our enterprise AI standards.In this newly created role, you will help move the organisation from promising pilots to a governed, compounding portfolio of agents that genuinely add value—no fluff, no hype, no rework.Get Excited to build agents that matter: Design and ship production AI agents across the taxonomy: conversational/user-facing, knowledge & Q&A, workflow/process automation, decision-support, and monitoring.Reengineer and optimise the underlying business process first (mapping it in BPMN), then automate it—because automating a broken process just makes waste faster.Integrate tools and MCP connectors with least-privilege, allow-listed access, and clean separation between agents that read and agents that act.Build retrieval-augmented generation (RAG), vector, and data pipelines grounded in governed data, so agents answer from a single source of truth.Contribute to creating agents that generate dashboards, answer business questions, and release insights from enterprise data and the governed semantic layer on Databricks, with evaluation, cost monitoring, and release controls built into the delivery process.Note: Data science and machine learning experience is a strong plus, particularly where it supports forecasting, segmentation, recommendation, decision-support, or user intelligence use cases.Make the Rollout Enterprise: Build the evaluation harness: quality, groundedness, safety, bias, and prompt-injection testing, combining LLM-as-judge with human review, so agents earn their release.Establish versioning and CI/CD for everything—agents, prompts, tools, MCPs, and pipelines with environments, release gates, rollback, and kill-switches.Stand up observability and AgentOps: telemetry, monitoring, cost tracking, drift detection, and continuous improvement loops in production.Define repeatable, supervised delivery patterns and progressively embed them into day-to-day engineering and operational practice.Engineer for Trust, Cost, and Scale: Build with security, privacy, and governance by design—human-in-the-loop by default for anything touching end-users, financials, or systems of record; full logging and traceability.Manage performance, reliability, and cost in production; reduce recurring issues through standard patterns.Document thoroughly—runbooks, agent cards, decision records—so what you build is supportable and reusable.Raise the Whole Organisation's Game: Partner with and coach business AI champions; where a high-value area is stuck, embed temporarily to deliver the first agent and hand it back.Contribute SME insight, reusable patterns, and delivery learnings into our enterprise AI standards and guardrails through established architecture and governance forums.
Qualifications
3+ years’ experience in a fast-paced, large-scale commercial or enterprise environment.Proven experience designing, building, and shipping LLM/agent applications to production (not just prototypes).
Required Skills
Strong software engineering fundamentals in Python and/or TypeScript (or similar), with solid API and version-control practice.Hands-on with agent frameworks and orchestration, tool/function calling, and the Model Context Protocol (MCP).RAG, embeddings, and vector databases, plus strong SQL and data pipeline skills; comfortable working with structured and unstructured data.Evaluation and LLMOps / AgentOps: building eval harnesses, versioning prompts and agents, CI/CD, observability, and guardrails for AI systems at scale.Prompt and context engineering, with an eval-driven, iterative approach.Strong analytical and problem-solving skills.Results-driven with excellent organisation and prioritisation abilities.Calm, adaptable, and effective in fast-paced environments.Proactive mindset with strong initiative and accountability.Commercially astute with sound business judgement.Collaborative communicator, able to engage technical and non-technical stakeholders.