Forward Deployed AI Engineer

Accenture; Greater London; On-site; GBP 90,000 - 150,000; Full time

Job summary

Accenture is seeking a Forward Deployed AI Engineer to join our Reinvention Deployment Engineering pods in London. You will drive enterprise AI transformations, translating platform capabilities into measurable business value and scalable production systems across client enterprises.

You will own deployment outcomes, collaborate with client executives, and define architectural standards to ensure multi-platform integration and governance.

Qualifications

• Experience with cloud-native systems and modern architectures.
• Design and deployment of agentic AI solutions in production.
• Hands-on work with leading AI platforms (OpenAI, Claude, Vertex AI).
• History of leading software engineering teams and delivering end-to-end outcomes.
• Ability to quantify and communicate business value to senior stakeholders.

Responsibilities

• Own account-level AI transformation across the client enterprise—from platform selection through adoption and reinvention at scale.
• Hold full accountability for deployment outcomes: time-to-value, reliability, adoption, ROI; report to client leadership.
• Translate strategic intent into production AI systems deployed across the enterprise.
• Define enterprise AI architecture standards and governance across identity, data, security, and lifecycle management.
• Engage with client CTO/CFO/CISO; shape AI investment strategy and reinvention roadmaps.
• Codify reusable patterns and playbooks that scale across markets and practice globally.
• Lead executive workshops and board-level architecture reviews shaping the AI pathway.

Skills

Cloud-native systems

Agentic solutions design

AI platforms

End-to-end delivery ownership

Stakeholder communication

Leadership of software teams

Business value articulation

Tools

OpenAI

Claude

Vertex AI

Job description

This is not a consulting role. It is not a project delivery role. It is not a research position. A Forward Deployed AI Engineer is a production engineer who works embedded inside a client's enterprise, shoulder to shoulder with their teams, to make complex AI platforms work in real, messy organizational environments. You own outcomes: time-to-value, adoption, reliability, and scalability. Not delivery milestones. Outcomes.

The market is beginning to understand what leading technology companies have demonstrated: AI products fail not because the models are weak but because deployment is broken. The gap between a successful AI pilot and an AI capability that scales is bridged by engineers who can translate platform capability into measurable business value inside a real enterprise environment. That is this role.

Forward Deployed AI Engineers form the execution spine of our Reinvention Deployment Engineering pods. We are building the largest FDE capability in the services industry. The engineers who join at this stage will define what the role looks like at scale and will have access to the hardest enterprise AI problems in the market across every industry.

Key Responsibilities

• Own account-level AI transformation across the client enterprise — from platform selection and architecture through to enterprise-wide adoption and reinvention at scale
• Hold full accountability for deployment outcomes at account level: time-to-value, reliability, adoption, and ROI — directly reported to client executive leadership
• Drive ambiguity to resolution at account scale: translate executive-level strategic intent into production AI systems operating across the enterprise
• Define enterprise AI architecture standards and governance frameworks across the client organisation, spanning identity, data, security, multi-platform integration, and AI lifecycle management
• Own the relationship with client CTO, CFO, and CISO: shape AI investment strategy, board-level narrative, and multi-year reinvention roadmap
• Codify reusable patterns and playbooks that define what enterprise AI reinvention looks like — assets that scale across markets, industries, and the FDE practice globally
• Lead executive workshops, board-level engagements, and cross-practice architecture reviews that shape the client’s AI reinvention trajectory
• Define the FDE practice model: engineering standards, deployment playbooks, talent development frameworks, and the capability architecture that the practice is built on

Basic Qualifications

• Experience with cloud-native systems (APIs, microservices, containerization, serverless).
• Expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.
• Significant experience with AI platforms — OpenAI, Claude, Vertex AI, plus open-source models, including building abstraction layers to manage multi-provider pipelines.
• A history of leading software engineering teams: overseeing delivery, allocating resources across workstreams, and owning the professional development of direct reports
• Demonstrated end-to-end delivery ownership in a client-embedded environment, internal projects, vendor labs, or team-only deployments do not qualify
• Proven ability to articulate business value: can quantify the impact of deployments in terms a CFO would recognize and action
• Experience presenting to and building trust with senior client stakeholders, CTO, CFO, or CISO level
• Non-linear profiles are expected and welcomed, assessment is based on demonstrated deployment experience and outcome ownership, not CV pattern matching