Senior 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 Singapore. Candidates must live and work in Singapore and be legally authorized to work in Singapore.
The Opportunity
As a Senior Forward Deployed Engineer at Stacklok, you sit where platform engineering meets AI, bringing deep Kubernetes expertise to help enterprises adopt Stacklok Enterprise and run AI agents securely on the infrastructure they already trust. Every environment is different, so the role builds the custom operators that fit the platform to each customer's stack and reach production fast.
This is hands-on, high-ownership work, and it is not only technical. Forward-deployed engagements run end to end through the role: standing up custom proof-of-concepts for Design Partners and customers, extending the platform, coordinating the technical work, and keeping customer and team aligned as things move toward production.

When unlocking value means changing how the product deploys to Kubernetes, that work comes back to the platform itself.
Kubernetes expertise is what unlocks value fast, and demand for this work is outpacing our capacity. The role offers a front-row seat to how leading enterprises put AI into production, and real influence over what gets built next. For a platform engineer who wants that expertise to land directly with customers, this is the chance to do it at the frontier of enterprise AI.
What Success Looks Like: First 6-12 Months
- Ramped quickly on the platform and Stacklok Enterprise architecture, becoming the technical SME the AppliedAI team relies on for enterprise engagements.

- Built trusted relationships across the team and with customers, and resolved a real customer blocker or shipped a first meaningful change.

- Took one or more Design Partners from proof-of-concept to a working production deployment of Stacklok's Enterprise platform.

- Unblocked enterprise adoption by improving how the product deploys to Kubernetes, measurably cutting time-to-value for customers.

- Turned field learnings into a reusable engagement playbook and tooling, using AI-assisted workflows to make the next deployment faster for the whole team.

In This Role, You Will
- Drive forward-deployed engagements end to end, from scoping each customer's technical goals and designing the deployment approach to taking POCs to a working state and toward production.

- Design and deliver changes to the platform and to how it deploys to Kubernetes, unblocking enterprise adoption and contributing those changes back to Stacklok Enterprise.

- Act as the technical SME for enterprise adoption, answering deep platform and Kubernetes questions and giving hands-on support to customers and Design Partners.

- Own the technical calls within your domain, moving quickly on reversible decisions and pulling in the team anchor as scope and risk grow.

- Partner with senior peers and bring field insight back through standup, planning, and desig