Senior Forward Deployed Engineer - Kubernetes

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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 platforn, 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 Stackok 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 design reviews to influence priorities and how engag

Stacklok由CEO Craig McLuckie和CTO Joe Beda领导，他们是Kubernetes的两位创造者。随着AI重塑软件的构建和使用方式，我们正在构建企业自信采用AI所需的基础。我们正在构建企业AI agent的控制平面，使组织能够在他们已经信任的基础设施上运行、治理和保护这些agent。通过运行在Kubernetes和私有云环境中的Model Context Protocol（MCP）服务器，AI agent可以安全地连接内部数据和系统，同时满足高度受监管和安全意识强的组织在安全、合规和运营方面的要求。我们还通过企业AI网关将这一基础扩展到模型层。Stacklok Enterprise Platform构建在我们的开源MCP平台ToolHive之上，并且已被领先的科技公司和受监管行业的组织采用。我们还帮助维护官方MCP注册表，并公开为塑造企业AI未来的社区做出贡献。 地点 这是一个位于新加坡的远程职位。候选人必须在新加坡居住和工作，并拥有在新加坡合法工作的授权。 机会 作为Stacklok的高级Forward Deployed Engineer，你处于平台工程与AI的交汇点，带来深厚的Kubernetes专业知识，帮助企业采用Stacklok Enterprise并在他们已经信任的基础设施上安全运行AI agent。每个环境都不同，因此该角色需要构建自定义operator，使平台适配每个客户的技术栈，并快速投入生产。这是高度动手、高自主权的工作，而且不仅仅是技术性的。Forward-deployed合作项目通过该角色端到端运行：为Design Partners和客户搭建自定义proof-of-concept、扩展平台、协调技术工作，并在事情推进到生产时保持客户和团队保持一致。当释放价值意味着改变产品部署到Kubernetes的方式时，这项工作会回到平台本身。Kubernetes专业知识是快速释放价值的关键，而这项工作需求正在超过我们的 capacity。该角色提供前排视角，了解领先企业如何将AI投入生产，并对下一步构建什么产生真正影响。对于希望将这种专业知识直接落地到客户的平台工程师来说，这是在企业AI前沿做到这一点的机会。 成功的样子：前6-12个月 快速熟悉平台和Stacklok Enterprise架构，成为AppliedAI团队在企业合作中依赖的技术SME。 在团队内部和与客户之间建立可信关系，并解决了一个真实的客户阻塞问题或交付了第一个有意义的变更。 将一个或多个Design Partners从proof-of-concept带到Stacklok Enterprise平台的可工作生产部署。 通过改进产品部署到Kubernetes的方式来解除企业采用的阻塞，可衡量地缩短客户的time-to-value。 将现场学习转化为可复用的合作playbook和工具，使用AI辅助工作流让整个团队的下一次部署更快。 在这个角色中，你将 端到端推动forward-deployed合作项目，从界定每个客户的技术目标和设计部署方法，到将POC带到可工作状态并推向生产。 设计并交付对平台以及其部署到Kubernetes方式的变更，解除企业采用的阻塞，并将这些变更回馈到Stacklok Enterprise。 作为企业采用的技术SME，回答深层次的平台和Kubernetes问题，并为客户和Design Partners提供动手支持。 在你领域内主导技术决策，对可逆决策快速行动，并随着范围和风险增长引入团队anchor。 与高级同侪合作，并通过standup、规划和设计评审带回现场洞察，以影响优先级和合作方式。

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