Forward Deployed Engineer

Responsibilities: Embed with client teams to deploy and scale AI platforms in enterprise environments Own end-to-end production outcomes with business metrics Turn ambiguous problems into production-ready systems through rapid prototyping Design and govern AI architectures across identity, data, security, governance, and workflow layers Translate technical architecture into business impact for client leadership; shape ROI backlogs Create reusable patterns, playbooks, and accelerators for client use after engagement Lead design workshops, PoCs, architecture walkthroughs, and code-with sessions with client teams Codify patterns and lessons learned to scale the FDE practice

Key requirements: 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 multi-provider pipelines Production deployments experience, CI/CD, infrastructure as code (Terraform, Helm) End-to-end delivery ownership in client-embedded environments Ability to quantify deployment impact in business terms recognizable by executives Experience presenting to senior client stakeholders (CTO, CFO, CISO) Non-linear profiles welcome; emphasis on deployment outcomes over CV patterns Stakeholder management Clear business-value communication Collaboration across cross-functional teams Cloud-native architectures (APIs, microservices, containers, serverless) Agentic computing and orchestration, context engineering, RAG AI platforms and multi-provider integration (OpenAI, Claude, Vertex AI)