AI Engineer ( Manager

UKI Finance RP Finance AI Engineer The practice Finance is one of the most demanding and valuable environments in which to apply modern technology. You will work with complex enterprise data, mission-critical processes and high-impact decisions, using AI, data and engineering to reshape how organisations plan, control performance and allocate resources. The opportunity goes beyond building technically strong solutions: you will see how those solutions influence cash, profitability, risk and business growth, and take them from experimentation into trusted, production-ready capabilities.

Working in Finance Reinvention allows you to remain close to leading-edge technology while developing an understanding of the CFO agenda, gaining exposure to senior decision-makers and building the commercial judgement needed to solve enterprise-wide challenges. This combination of deep technical capability, finance-domain expertise and measurable business impact creates a differentiated career path that is difficult to develop in a purely technology-focused role.

Core build capability for the practice. Responsible for engineering agents, the orchestration around them, the evaluation harness that establishes whether they perform, and the finance user surfaces through which they are operated. The role designs, develops, tests, deploys and operates production-grade AI applications and the services around them. Embedded within a delivery pod and working on client data from an early stage of each engagement, the role translates finance-process and control requirements into secure, observable and maintainable software.

• Build and deploy agentic workflows into live finance processes, including reconciliations, journals, exception handling, disputes, collections and variance explanation, with clear acceptance criteria, human oversight and controlled failure behaviour.
• Implement LLM application patterns including tool and function calling, structured outputs, RAG or graph-based retrieval, context and memory management, multi-agent orchestration, and prompt or configuration versioning.
• Integrate with ERP and EPM systems, document repositories and workflow tooling, including the data extraction, transformation and error handling that integration requires, using secure APIs, events, identity controls and reusable connectors where appropriate.
• Write evaluations, instrument agent behaviour, and ensure failure modes are visible and recoverable, using automated functional and non-functional tests, trace-level observability, red-team scenarios and measures for quality, groundedness, latency and cost.
• Build the interfaces through which finance users review, approve, override and evidence agent actions, including provenance, citations, feedback capture, accessibility and clear error-recovery journeys.
• Package and operate solutions using cloud AI services, containers or serverless components, CI/CD, infrastructure as code, secrets and configuration management, monitoring, release controls and rollback.
• Work directly with finance users, process specialists, architects and data engineers to clarify requirements, challenge assumptions constructively, demonstrate increments and document operating procedures and runbooks.
• Contribute reusable patterns and components back to the practice asset base, including tested code, evaluation assets, reference implementations and implementation guidance.

Strong software engineering discipline, including Python and at least one additional production language such as Type Script or equivalent, version control, testing, CI, and maintainable code, together with SQL, API design, code review and secure coding practices. Hands-on LLM application development, covering tool use, structured outputs, retrieval and agent frameworks, including embeddings, vector or graph retrieval, prompt and configuration management, and integration with enterprise data or services.

Practical experience with evaluation, observability and LLMOps or Agent Ops, including versioning, monitoring, release and rollback, and analysis of quality, latency and cost. Understanding of secure enterprise integration and sensitive-data handling, including authentication, authorisation, secrets, logging and PII-aware design. Ability to work client-side and collaborate effectively with non-technical finance users, translating their needs into testable requirements while explaining options, risks and trade-offs clearly.

At…