Accenture UK & Ireland
Forward Deployed AI Engineer
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Forward Deployed AI Engineer
# Forward Deployed AI Engineer
AI platforms fail due to deployment, not model strength. This is not a consulting, project delivery, or research role. A Forward Deployed AI Engineer is a production engineer embedded in a client’s enterprise, collaborating directly with their teams to ensure AI platforms deliver real, measurable business value—owning outcomes, not milestones.
About the Role
The market is harnessing what leading technology firms already know: AI-as-a-product succeeds only when deployment integrates seamlessly into enterprise workflows. This role bridges the gap between AI models and scalable, production capabilities—translating technical potential into actionable, measurable business results across complex organisations.
Forward Deployed AI Engineers (FDE) are the operational core of the Reinvention Deployment Engineering pods. This is shaping the largest enterprise AI deployment capability globally, offering hands-on exposure to the most challenging AI challenges across industries.
Key Responsibilities
Programme Leadership & Strategy
- Lead enterprise AI platform deployments across awkward multi-stakeholder client environments (Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, Palantir)—owning the full lifecycle from architecture.
- Drive programme-level outcomes (time-to-market, reliability, adoption, scalability) across multi-concurrent workstreams, linked to commercial KPIs.
- Empower rapid experimentation outcomes at velocity: resolve ambiguous business problems into production-grade systems within days/weeks.
- Design and govern AI solutions across the full tech stack: identity, data, security, governance, platform orchestration, and cross-system orchestration at scale.
- Shape AI reinvention strategy for CTOs, CFOs, CISOs—producing value architectures, ROI backlogs, use case prioritisation frameworks, and long-term AI adoption roadmaps.
- Develop scalable reinvention blueprints, patterns and accelerators reused across engagements to propel FDE capability growth.
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Ownership & Execution
- Lead architecture design sessions, executive workshops, and code-with customer sessions with client engineering and C-suite leadership.
- Codify delivery learnings, failure post-mortems, and engineering standards to refine the FDE practice and mentor future generations.
Basic Qualifications
Technical Experience Required
- Proven experience in cloud-native systems development: APIs, microservices, containerisation, and serverless.
- Deep expertise in building and scaling agentic solutions in production (LLM agents, orchestration, context engineering, RAG, workflows).
- Hands-on experience with AI platforms (OpenAI, Vertex AI, Claude) and cross-vendor model orchestration.
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Leadership & Delivery Mindset
- Delivered full-lifecycle AI/ML projects in client-embedded or end-to-end delivery environments (vendor labs, team-only deployments unwelcome).
- Articulate business impact: Quantify end-to-end outcomes in a way CFOs or CEOs care about (e.g., quantifiable gains like reduced turnaround time, productivity, or revenue retention).
- Execute at executive level: Build trust and shape decisions alongside CTOs, CFOs, and CISOs—co-locate on-site or virtually.
- Self-imposed by outcomes, not traditional metrics or CV patterning. Prioritise experience driving large-scale AI/ML production projects.
People Leadership & Accountability
- Experience managing, developing, and performance-managing engineering teams (10+ reports ideal).
- Individual development plans and career progression conversations.
Non-linear career paths and context-proven expertise are encouraged. Assessments prioritise outcomes and ownership over degrees or certifications.
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