Finance AI Engineer: Enterprise LLM & Automation Architect

Accenture PLC; Manchester; Vor Ort; GBP 70.000 - 110.000; Vollzeit

Zusammenfassung

Accenture PLC is seeking a Finance AI Engineer to design, develop, test and operate production-grade AI applications for finance processes. You will build agentic workflows, implement LLM patterns, and integrate with ERP/EPM systems while ensuring secure, observable, and maintainable software.

You will collaborate with finance users, process specialists and data engineers to deliver measurable business impact, covering cost, latency and quality considerations in a regulated enterprise

Qualifikationen

• 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 or 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 4 years' relevant professional experience

Aufgaben

• 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.

Kenntnisse

Python

TypeScript

SQL

API design

LLMOps

AgentOps

CI/CD

Observability

Secure coding

Cloud services

Tools

Git

Docker

Kubernetes

Azure

AWS

GCP

Databricks

Snowflake

Jobbeschreibung

Accenture PLC is seeking a Finance AI Engineer to design, develop, test and operate production-grade AI applications for finance processes. You will build agentic workflows, implement LLM patterns, and integrate with ERP/EPM systems while ensuring secure, observable, and maintainable software.

You will collaborate with finance users, process specialists and data engineers to deliver measurable business impact, covering cost, latency and quality considerations in a regulated enterprise