Finance AI Engineer ( Manager )

Job Description

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.

Purpose of the 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.

Responsibilities

• 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.

Essential experience

• Strong software engineering discipline, including Python and at least one additional production language such as TypeScript 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 AgentOps, 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 least 6 years’ relevant professional experience

Desirable

• Front-end capability alongside back-end, sufficient to deliver a usable interface independently, for example React, Next.js or an equivalent framework.

• Prior exposure to SAP, Oracle, Workday, Anaplan or OneStream, or to finance processes such as close, planning, order to cash or procure to pay.

• Experience with MCP, graph or semantic retrieval, data engineering, event-driven integration or workflow orchestration.

• Experience deploying software or data products on Azure, AWS or GCP, DataBricks, Snowflake, Palantir etc. using relevant cloud services and at least one of containers, Kubernetes or serverless patterns.

• Delivery experience in a regulated environment or under formal responsible-AI, security or model-risk controls.

Qualification

N/A

Locations

London

Manchester

Additional Information

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