Forward Deployed Engineer Specialist

ph3Role Description /h3 pThis 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. /p pThe 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 role. /p pForward 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. /p h3Key Responsibilities /h3 ul liEmbed directly with client engineering and business teams to deploy, scale, and operationalize AI platforms — Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, or Palantir — inside enterprise environments /li liOwn production outcomes end-to-end: time-to-value, reliability, adoption velocity, and scalability, with business metrics attached — not just delivery milestones /li liMove from ambiguous business problem to working production system through rapid experimentation: days to prototype, weeks to production-ready /li liDesign and govern AI architectures across the full enterprise stack: identity, data, security, governance, platform layer, and workflow integration /li liTranslate technical architecture into business impact for client CTO, CFO, and CISO; shape use case roadmaps, ROI backlogs, and AI adoption strategy /li liBuild reusable patterns, playbooks, and accelerators that the client owns after you leave — enabling the client team to run it without you /li liLead design workshops, proofs of concept, architecture walkthroughs, and code-with sessions with client engineering and leadership teams /li liCodify patterns and delivery learnings that scale across engagements and contribute to the growth of the FDE practice /li /ul pCompensation at Accenture varies depending on a wide array of factors including but not limited to role, level, seniority, responsibility, skillset, and level of experience. For this position, the job level is under the B2 National Collective Bargaining Agreement. The gross annual salary range is between 32,000 and 54,900 EUR. /p h3Basic Qualifications /h3 ul liDemonstrable engineering experience with cloud-native systems (APIs, microservices, containerization, serverless). /li liDeep expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments. /li liDemonstrable experience with AI platforms — OpenAI, Claude, Vertex AI, plus open-source models — including building abstraction layers to manage multi-provider pipelines. /li liExtensive and demonstrable experience of deploying to production , CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging. /li liDemonstrated end-to-end delivery ownership in a client-embedded environment, internal projects, vendor labs, or team-only deployments do not qualify /li liProven ability to articulate business value: can quantify the impact of deployments in terms a CFO would recognize and act on /li liExperience presenting to and building trust with senior client stakeholders, CTO, CFO, or CISO level /li liNon-linear profiles are expected and welcomed, assessment is based on demonstrated deployment experience and outcome ownership, not CV pattern matching /li /ul /p #J-18808-Ljbffr