Forward Deployed AI Engineer

This 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.     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 role.   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 role looks like at scale and will have access to the hardest enterprise AI problems in the market across every industry.

Embed 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   Own production outcomes end-to-end: time-to-value, reliability, adoption velocity, and scalability, with business metrics attached — not just delivery milestones   Move from ambiguous business problem to working production system through rapid experimentation: days to prototype, weeks to production-ready   Design and govern AI architectures across the full enterprise stack: identity, data, security, governance, platform layer, and workflow integration   Translate technical architecture into business impact for client CTO, CFO, and CISO; shape use case roadmaps, ROI backlogs, and AI adoption strategy   Build reusable patterns, playbooks, and accelerators that the client owns after you leave — enabling the client team to run it without you   Lead design workshops, proofs of concept, architecture walkthroughs, and code-with sessions with client engineering and leadership teams   Codify patterns and delivery learnings that scale across engagements and contribute to the growth of the FDE practice     3-5   years   engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).   Minimum of 1   years   of deep   expertise   in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.   Minimum of 3 years of experience with AI platforms — OpenAI, Claude, Vertex AI, plus open-source models — including building abstraction layers to manage multi-provider pipelines.   Minimum of 10 years of experience deploying to   production ,   CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.   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   #LI-MP   About Accenture Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and