Forward Deployed AI Engineer

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 scalesisbridged 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.

• Lead enterprise AI platform deployments across complex multi-stakeholder client environments — Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, or Palantir — owning the fullprogrammefrom architecture through adoption

• Ownprogramme-level delivery outcomes: time-to-value, reliability, adoption velocity, and scalability across multiple concurrent work streams, 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 atprogrammescale

• Shape AI reinvention strategy for client CTO, CFO, and CISO: build value architecture, ROI backlogs, use caseprioritisationframeworks, 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).

• Deepexpertisein 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 work streams, 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 ondemonstrateddeployment 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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