Owns strategic enterprise AI deployments from scoping through production go-live and customer self-sufficiency. Responsibilities include defining scope and exit criteria, managing multi-workstream delivery plans, customer dependencies, risks, budgets, capacity, testing, validation, training, and executive communication. The role directs forward-deployed and customer solutions engineers, supports expansion planning, develops reusable delivery patterns, and ensures successful adoption across regulated enterprise environments.

Build and maintain a backend-first fullstack platform for governed AI workflows: design Python/Django services, APIs, Kubernetes-backed execution, agent orchestration, permission-aware data access, observability, frontend surfaces in TypeScript/React, and platform abstractions to make distributed AI workflows reliable, auditable, and usable by regulated customers.

Own and operate the deployment backbone for a multi-environment AI platform: build CI/CD pipelines, ensure multi-cloud deployment parity (SaaS, VPC, on-prem), automate infrastructure provisioning, implement observability and security, run incident response and on-call, collaborate with developers to improve reliability, and maintain documentation and runbooks.