Forward Deployed AI Engineer

Accenture UK & Ireland; Newcastle upon Tyne; On-site; GBP 120,000 - 180,000; Full time

Job summary

Accenture UK & Ireland invites a Forward Deployed AI Engineer to embed with client teams, turning AI platform capabilities into real business value in complex environments. You own time-to-value, adoption, reliability, and scalability across multiple workstreams, shaping blueprints and accelerators for the FDE practice.

This role partners with CTO/CFO/CISO stakeholders to drive enterprise-scale deployments across industries and will influence how AI capabilities scale inside client organizations.

Qualifications

• Engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).
• Deep expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.
• Experience with AI platforms — OpenAI, Claude, Vertex AI, plus open-source models — including building abstraction layers to manage multi-provider pipelines.
• Experience 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 or vendor labs do not qualify
• Proven ability to articulate business value: quantify impact of deployments for CFO-level decisions
• Experience presenting to senior client stakeholders (CTO, CFO, CISO)
• Non-linear profiles welcomed; assessment based on deployment experience and outcome ownership
• People lead responsibilities: managing, developing, and performance-managing a team of engineers

Responsibilities

• Lead enterprise AI platform deployments across complex multi-stakeholder client environments, owning the full programme from architecture through adoption.
• Own programme-level delivery outcomes: time-to-value, reliability, adoption velocity, and scalability across multiple concurrent workstreams, with commercial metrics.
• Lead rapid experimentation at pace: drive ambiguous business problems to working production systems in days or weeks.
• Architect and govern enterprise AI solutions across the full technology stack: identity, data, security, governance, platform layer, and multi-system workflow integration at programme scale.
• Shape AI reinvention strategy for client CTO, CFO, and CISO: build value architecture, ROI backlogs, use case prioritisation frameworks, and multi-year AI adoption roadmaps.
• Define and publish reusable reinvention blueprints, patterns, and accelerators that scale across multiple client engagements and grow the FDE practice.
• Lead architecture design sessions, executive workshops, and code-with sessions with client engineering and C-suite leadership teams.
• Codify delivery learnings, failure patterns, and engineering standards that shape the FDE practice and enable the next generation of forward deployed engineers

Skills

Cloud-native APIs

Microservices

Containerization

Serverless

Agentic AI solutions

Orchestration

RAG integration

Production deployment

Stakeholder communication

Delivery ownership

Business value articulation

Leadership

Job description

Role 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

• Lead enterprise AI platform deployments across complex multi-stakeholder client environments — Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, or Palantir — owning the full programme from architecture through adoption
  
• Own programme-level delivery outcomes: time-to-value, reliability, adoption velocity, and scalability across multiple concurrent workstreams, with commercial metrics attached
  
• Lead rapid experimentation at pace: drive ambiguous business problems to working production systems in days or weeks across complex enterprise environments
  
• Architect and govern enterprise AI solutions across the full technology stack: identity, data, security, governance, platform layer, and multi-system workflow integration at programme scale
  
• Shape AI reinvention strategy for client CTO, CFO, and CISO: build value architecture, ROI backlogs, use case prioritisation frameworks, and multi-year AI adoption roadmaps
  
• Define and publish reusable reinvention blueprints, patterns, and accelerators that scale across multiple client engagements and grow the FDE practice
  
• Lead architecture design sessions, executive workshops, and code-with sessions with client engineering and C-suite leadership teams
  
• Codify delivery learnings, failure patterns, and engineering standards that shape the FDE practice and enable the next generation of forward deployed engineers
  

Basic Qualifications

• Engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).
  
• Deep expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.
  
• Experience with AI platforms — OpenAI, Claude, Vertex AI, plus open-source models — including building abstraction layers to manage multi-provider pipelines.
  
• Experience 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 act on
  
• 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
  
• People lead responsibilities: experience managing, developing, and performance-managing a team of engineers; setting individual development plans and conducting career conversations
  

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