AI Engineer at KPMG, London, 6 Months, £Contractor Rate

21 hours ago

Role Title: AI Engineer – Agentic Audit Platform

Contract Length:  6 months
Engagement Type:  Umbrella or PAYE
Location:  London

(ALL KPMG UK CONTRACTORS MUST RESIDE AND HAVE PROOF OF RIGHTS TO WORK IN THE UK)

This role combines technical leadership with hands-on AI engineering and stakeholder collaboration, to deliver Audit innovation.

The AI Engineer works in a forward deployed capacity, embedded directly with audit business domain experts and engagement teams to engineer and deliver AI solutions that demonstrate business value and innovation at pace. You will develop, configure, and operate certified AI platform building blocks within live engagement environments, applying software engineering best practices while working closely with architects, methodology experts, and stakeholders across varying seniority levels.

You will consume architectural designs (HLDs, LLDs, ADRs), deploy solutions to engagement environments, and provide first-line operational support while feeding real-world learnings back into the reference architecture and engineering roadmap. Working at the forefront of AI-driven audit transformation, you'll bridge the gap between innovative technology and practical business application.

This is a contingent position for a specific project, requiring a can-do attitude, strong collaboration skills with internal and external stakeholders, and readiness to engineer solutions in a dynamic, forward deployed environment.

Responsibilities:

Platform Operations & Deployment

• Manage certified Agentic Platform building blocks through their lifecycle: promotion, packaging, registration, version pinning, and deprecation
• Execute engagement deployment runbooks, including database provisioning, schema application, workflow configuration, and snapshot loading
• Configure workspaces, access control, roles, and per-engagement data-layer administration
• Maintain building-block documentation and validate deployments end-to-end using verification checklists

Embedded Delivery & Stakeholder Partnership

• Operate in a forward-deployed capacity, embedded within audit engagements to deliver agentic solutions alongside engagement teams and audit SMEs.
• Translate business and audit requirements into configured agent workflows, prompts, tools, and platform capabilities aligned to engagement objectives.
• Prototype, test, and iteratively refine solutions through close collaboration with end users and rapid feedback cycles.
• Drive adoption by coaching users on platform ways of working, operational hygiene, and reproducible practices.
• Communicate technical concepts effectively to stakeholders across varying levels of seniority, bridging business needs and engineering implementation.
• Capture recurring themes and enhancement opportunities from engagements, feeding structured insights into platform architecture and product evolution

Engineering Discipline & Platform Standards

• Operate against architectural documentation (HLDs, LLDs, ADRs), raising change requests when design and reality diverge
• Apply SDLC standards including Git workflows, testing, reproducible releases, configuration-as-code, and regression suites
• Enforce platform guardrails: naming conventions, prompt patterns, JSON-schema contracts, DDL contracts, and AOP shape
• Apply prompt, data, and context engineering within architectural patterns set by the Architect

Incident Management & Observability

• Provide first-line support and triage incidents across AOP runs, databases, and agent working-memory
• Investigate issues using logs, traces, and evaluation outputs, escalating reproducible defects to platform engineering or third-party suppliers where required.
• Maintain platform observability, monitoring log volumes, tool-call distributions, and environment health
• Support sign-off workflows, evaluation engines, and working-paper integrity without compromising immutability contracts

Experience and Knowledge requirements:

Required

Technical:

• Strong software engineering background with demonstrable SDLC knowledge (Git, testing, CI/CD, configuration-as-code)
• Hands-on fluency with SQL, PostgreSQL, JSON/YAML configuration, and Python
• Experience developing AI/ML solutions using third-party platforms and APIs, including prompt engineering patterns
• Ability to run, debug, and contribute to orchestration runtimes
• Systems thinking with experience running production data or AI systems
• Understanding of schemas, information architecture, and context-block patterns
• Experience with RESTful APIs, cloud services, RAG architectures, and LLM application patterns

Architecture & Operations:

• Comfort working from architectural documentation (HLDs, LLDs, ADRs) and identifying design-reality divergence
• Incident management, troubleshooting, and structured escalation experience with strong documentation habits

Collaboration & Communication:

• Proven ability to work in a forward deployed capacity, embedded closely with business domain experts and engagement teams
• Experience collaborating with diverse internal and external stakeholders, translating requirements into technical solutions
• Ability to coach non-technical users on complex systems and enable them to operate confidently
• Strong written and verbal communication across technical and non-technical audiences

Behavioural:

• Can-do attitude with bias toward action, innovation, and demonstrating business value at pace
• Operational ownership mindset focused on safe, repeatable, observable delivery
• Calm, structured approach to incident triage and escalation
• Self-motivated with strong initiative, adaptability, and resilience in dynamic environments

Desirable

• Prior audit, financial services, or regulated-domain operational experience
• Experience in architecture frameworks and systems thinking (TOGAF, AWS/Azure/GCP certifications)
• Previous forward deployed, field engineering, or consulting experience working directly with clients or business units
• UX literacy including journey mapping and wireframing
• Open-source contributions to LLM agent platforms (LangChain, LlamaIndex, Anthropic SDK)
• Full stack experience to develop the end-to-end applications using AI coding agents such as Claude Code, GitHub Copilot

Qualifications:

Relevant experience in operational engineering, platform engineering, data engineering, software engineering, AI systems operations or equivalent technology delivery roles is required. Certifications or formal training in cloud platforms, data engineering, software delivery, IT service management, security or governance frameworks are beneficial but not required.