Forward Deployed Eng Director

This is not a consulting . It is not a project delivery . 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 .

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 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

Significant 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.

Considerable experience with AI platforms - OpenAI, Claude, Vertex AI, plus open-source models, including building abstraction layers to manage multi-provider pipelines.

Substantial 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

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