Role Description
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.
Key Responsibilities
- 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
Required qualifications
- Minimum 5 years of engineering experience with cloud-native systems, including APIs, microservices, containerization, and serverless architectures
- Minimum of 1 year of experience 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, or open-source models), including building abstraction layers for multi-provider pipelines
- Minimum of 3 years of experience deploying systems to production, including CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging
- Demonstrated end-to-end delivery ownership in a client-embedded environment
- Experience embedding with client engineering or business teams to deploy AI solutions
- Experience working with enterprise AI platforms such as Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, or Palantir
- Experience designing and governing AI architectures across identity, data, security, and workflow integration
- Experience working with senior stakeholders (CTO, CFO, CISO) on AI-related initiatives
- Experience leading workshops, proofs of concept, or technical design sessions
- Experience developing reusable patterns, playbooks, or deployment accelerators
- Experience contributing to engineering standards or scalable delivery practices