Forward Deployed AI Engineer

Accenture PLC; Greater London; Hybrid; GBP 90,000 - 120,000; Full time

Job summary

Accenture PLC in London is seeking Forward Deployed AI Engineer to join our Reinvention Deployment Engineering capability. This role embeds with client teams to deploy and operationalize AI platforms in real enterprise environments.

You will own outcomes such as time-to-value, reliability, adoption velocity, and scalability, shaping AI adoption strategy and ROI with senior stakeholders.

Qualifications

• Experience deploying AI platforms in production and managing multi-provider pipelines.
• Ability to quantify business value of deployments to C-suite.
• Proven track record of end-to-end delivery ownership in client environments.
• Strong experience with CI/CD and infrastructure-as-code practices.

Responsibilities

• Embed directly with client teams to deploy AI platforms inside enterprise environments.
• Own production outcomes: time-to-value, reliability, adoption, scalability.
• Move from problem to production through rapid prototyping.
• Design architectures across identity, data, security, governance, and workflow.
• Lead design workshops and proofs of concept with client teams.

Skills

Cloud-native systems

Agentic solutions

AI platforms deployment

CI/CD

Infrastructure as code

Tools

Terraform

Helm

OpenAI

Claude

Vertex AI

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

• Embed directly with client engineering and business teams to deploy, scale, and operationalize AI platforms — Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, or Palantir — inside enterprise environments

• Own production outcomes end-to-end: time-to-value, reliability, adoption velocity, and scalability, with business metrics attached — not just delivery milestones

• Move from ambiguous business problem to working production system through rapid experimentation: days to prototype, weeks to production-ready

• Design and govern AI architectures across the full enterprise stack: identity, data, security, governance, platform layer, and workflow integration

• Translate technical architecture into business impact for client CTO, CFO, and CISO; shape use case roadmaps, ROI backlogs, and AI adoption strategy

• Build reusable patterns, playbooks, and accelerators that the client owns after you leave — enabling the client team to run it without you

• Lead design workshops, proofs of concept, architecture walkthroughs, and code-with sessions with client engineering and leadership teams

• Codify patterns and delivery learnings that scale across engagements and contribute to the growth of the FDE practice

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 deploying to production, CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.

• 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.