Forward Deployed Engineer Manager

About the role

Accenture is hiring a Forward Deployed Engineer Manager in Madrid. This is not a consulting, project delivery or research role: it is a production engineer embedded inside a client's enterprise, working alongside its teams to make complex AI platforms work in real, messy environments. You own outcomes such as time-to-value, adoption, reliability and scalability, not delivery milestones. The role leads enterprise AI platform deployments and shapes the Reinvention Deployment Engineering practice.

Key responsibilities

• Lead enterprise AI platform deployments across complex, multi-stakeholder client environments, owning the programme from architecture through adoption.
• Own programme-level outcomes: time-to-value, reliability, adoption velocity and scalability, with commercial metrics attached.
• Drive rapid experimentation, turning ambiguous business problems into working production systems in days or weeks.
• Architect and govern enterprise AI solutions across identity, data, security, governance, platform layer and multi-system workflow integration.
• Shape AI reinvention strategy for client CTO, CFO and CISO, including value architecture, ROI backlogs, use-case prioritisation and multi-year adoption roadmaps.
• Define reusable reinvention blueprints, patterns and accelerators that scale across client engagements.
• Lead architecture design sessions, executive workshops and code-with sessions with client engineering and C-suite teams.
• Codify delivery learnings, failure patterns and engineering standards that shape the practice.

Required profile

• Demonstrated end-to-end delivery ownership in a client-embedded environment; internal projects, vendor labs or team-only deployments do not qualify.
• Ability to articulate business value and present to CTO, CFO or CISO level stakeholders.
• Experience managing, developing and performance-managing a team of engineers, including individual development plans and career conversations.
• Non-linear profiles are welcomed; assessment is based on demonstrated deployment experience and outcome ownership.

Required skills

• Cloud-native systems: APIs, microservices, containerization, serverless.
• Agentic solutions in production: agents, orchestration, context engineering, RAG, workflows.
• AI platforms such as OpenAI, Claude, Vertex AI and open-source models, including abstraction layers for multi-provider pipelines.