Forward Deployed Engineer Manager
Pubblicato il 10-10-2026 - Accenture in Milano
Overview As a Forward Deployed AI Engineer, you embed with client teams to turn AI platform capabilities into measurable business value in real enterprise environments. You own outcomes such as time-to-value, adoption, reliability, and scalability across multiple workstreams, not just delivery milestones. You work on enterprise-scale AI deployments, shaping strategy and reusable patterns for the FDE practice. This role puts you at the interface of technology and business leadership, tackling hard AI adoption challenges and delivering production-ready solutions. You join a growing deployment engineering cohort that tackles high-impact, cross-industry AI problems.
Responsabilità
Lead enterprise AI platform deployments across complex client environments and partner ecosystems, owning architecture through adoption
Own programme-level delivery outcomes including time-to-value, reliability, adoption velocity, and scalability with commercial metrics
Drive rapid experimentation to transform ambiguous business problems into production systems within days or weeks
Architect and govern enterprise AI solutions across identity, data, security, governance, and multi-system integration at programme scale
Shape reinvention strategy for client leadership with value architectures, ROI backlogs, and multi-year adoption roadmaps
Define and publish reusable blueprints, patterns, and accelerators for scaling across client engagements
Lead architecture design sessions, executive workshops, and code-with sessions with client engineering and C-suite teams
Codify delivery learnings and engineering standards to evolve the FDE practice
Requisiti fondamentali
Extensive engineering experience with cloud-native systems (APIs, microservices, containerization, serverless)
Deep expertise in agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production
Experience with AI platforms—OpenAI, Claude, Vertex AI, and open-source models—and building abstraction layers for mult