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 engagements run.
  
  
• Coach teammates in your areas of deep expertise, especially Kubernetes, and contribute to hiring when needed.
  
  
• Apply AI-assisted workflows and strengthen reusable tooling and playbooks so each engagement moves faster for the team.

Desired Skill & Experience

We do not expect every candidate to meet every point below. If this role excites you and you bring most of it, we encourage you to apply.

• Strong software engineering fundamentals: writes well-structured production code with sound design and testing judgment.
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• Deep Kubernetes expertise across cluster architecture, workload types, networking, storage, RBAC, and resource management, with Helm authoring and GitOps via Flux or ArgoCD across managed Kubernetes.
  
  
• Strong operator and CRD literacy: reasons about control loops, CRD versioning, status, and finalizers, and writes controllers in Go using Kubebuilder or the Operator SDK.
  
  
• Solid grasp of observability across metrics, logs, and traces, and when each applies. Familiarity with Prometheus, OpenTelemetry, and platforms like Datadog or Grafana is a plus.
  
  
• Comfortable working directly with customers, including over-the-shoulder debugging across varied enterprise permission models, and clearly communicating root causes and remediation steps.
  
  
• A track record of delivering production-grade systems, mentoring engineers, and contributing fixes upstream to the underlying platforms and tools. Hands-on experience with MCP servers or AI agent tooling is a strong plus.
  
  
• AI-first mindset: an active user of AI coding assistants who brings agentic workflows (skills, agents, rules, hooks) into daily work and experiments with AI to automate and accelerate delivery.
  
  
• Communication: excellent written and verbal communication, able to explain complex technical ideas clearly to both technical and non-technical audiences.
  
  
• Startup mentality and intrinsic motivation: self-directed and hands-on, thrives in fast-moving, ambiguous environments, and drives clarity through action.

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