Staff Forward Deployed Engineer - Kubernetes

Stacklok is led by CEO Craig McLuckie and CTO Joe Beda, two of the creators of Kubernetes. As AI reshapes how software is built and used, we're building the foundation enterprises need to adopt it with confidence. We're building the control plane for enterprise AI agents, enabling organizations to run, govern, and secure them on the infrastructure they already trust. Through Model Context Protocol (MCP) servers running across Kubernetes and private cloud environments, AI agents can securely connect to internal data and systems while meeting the security, compliance, and operational requirements of highly regulated and security-conscious organizations. We've also extended this foundation to the model layer with an enterprise AI gateway. The Stacklok Enterprise Platform is built on ToolHive, our open source MCP platform, and is already being adopted by leading technology companies and organizations in regulated industries. We also help maintain the official MCP registry and contribute openly to the community shaping the future of enterprise AI.Location This is a remote role based in North America, with a strong preference for candidates located in the U.S. Eastern Time Zone. The role primarily supports customers across the Eastern Time Zone and EMEA. Regular travel is not expected. Occasional travel may be required for customer visits, company offsites, conferences, or other business needs. The Opportunity As a Staff Forward Deployed Engineer for Stacklok, you will serve as a technical lead for the US-East and EMEA region. This is hands-on work in the field: embedding with enterprise customers, taking them from first evaluation to production deployment, and answering the toughest technical questions that emerge along the way.  Beyond customer engagements, the role helps shape technical direction, establishes deployment patterns the broader organization builds on, and mentors engineers across the team. Field experience feeds directly into design reviews and roadmap discussions, ensuring customer realities help drive product and engineering decisions. The technical depth is substantial. The work spans complex Kubernetes deployments and the operational constraints of large enterprise environments, solving the problems that determine whether AI initiatives successfully reach production. It is an opportunity to shape how AI reaches production across some of the most demanding organizations in the region. What Success Looks Like: First 6-12 Months Onboarded quickly and led live engagements within the first 90 days, becoming the senior technical leader that customers and the field team rely on across the region. Took a Design Partner or enterprise customer from first evaluation to a working production deployment, clearing the hardest Kubernetes and platform blockers along the way. Stood up the in-region engagement model from scratch: the runbooks, escalation paths, and deployment standards that every engagement now follows. Raised the technic...