Forward Deployed Engineer

Role DescriptionThis 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.Youown outcomes: time-to-value, adoption, reliability, and scalability. Not delivery milestones. Outcomes.The market is beginning to understand what leading technology companies havedemonstrated: 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 scalesisbridged 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.Key ResponsibilitiesEmbed directly with client engineering and business teams to deploy, scale, and operationalize AI platforms — Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, or Palantir — inside enterprise environmentsOwn production outcomes end-to-end: time-to-value, reliability, adoption velocity, and scalability, with business metrics attached — not just delivery milestonesMove from ambiguous business problem to working production system through rapid experimentation: days to prototype, weeks to production-readyDesign and govern AI architectures across the full enterprise stack: identity, data, security, governance, platform layer, and workflow integrationTranslate technical architecture into business impact for client CTO, CFO, and CISO; shape use case roadmaps, ROI backlogs, and AI adoption strategyBuild reusable patterns, playbooks, and accelerators that the client owns after you leave — enabling the client team to run it without youLead design workshops, proofs of concept, architecture walkthroughs, and code-with sessions with client engineering and leadership teamsCodify patterns and delivery learnings that scale across engagements and contribute to the growth of the FDE practiceBasic QualificationsEngineering experience with cloud-native systems (APIs, microservices, containerization, serverless).Deep expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.Experience with AI platforms — OpenAI, Claude, Vertex AI, plus open-source models — including building abstraction layers to manage multi-provider pipelines.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 qualifyProven ability to articulate business value: can quantify the impact of deployments in terms a CFO would recognize and act onExperience presenting to and building trust with senior client stakeholders, CTO, CFO, or CISO levelNon-linear profiles are expected and welcomed, assessment is based on demonstrated deployment experience and outcome ownership, not CV pattern matching#LI-MP