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 Staff Forward Deployed Engineer for Stacklok, you will serve as the technical lead for APAC and help build the 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. As demand across APAC accelerates, you will own how Stacklok lands with every customer in the region.

Beyond customer engagements, the role helps shape technical direction across APAC, establish deployment patterns the broader organization builds on, and mentor 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 APAC 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 APAC engagement now follows.

Raised the technical bar across the region, lifting the engineers around them and turning field learnings into a reusable, AI-assisted playbook the whole team adopted.

• Surfaced APAC field insight, from region-specific regulatory needs to recurring customer requirements, that shaped a product or roadmap decision.

In This Role, You Will

Lead forward deployed engagements across APAC end to end, from scoping each customer's technical goals and deployment approach through to a working production deployment.

Own the region's technical calls as the in-region anchor and escalation point for the hardest platform and Kubernetes problems customers raise.

Design and deliver changes to how the platform deploys to Kubernetes, unblocking enterprise adoption and contributing those changes back upstream.

Define the reusable reference architectures and deployment patterns that engagements across the region and the wider team build on.

Set the technical bar for APAC, mentoring engineers through design reviews and pairing and serving as the technical gate for what good looks like in hiring.

Own how forward deployed work runs in the region, adapting it to how local customers operate and shaping where it hands off to applied AI engineering.

• Establish the AI-assisted practices and tooling the team adopts, and channel APAC field insight into the product roadmap.

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.

Extensive experience serving as the senior or lead engineer in enterprise customer engagements, with a proven ability to lead hands-on customer delivery in the field.

Proven technical leadership through technical direction, mentorship, and stewardship of high-quality engineering practices.

Deep Kubernetes expertise across cluster architecture, networking, storage, RBAC, and permission models, with operator and CRD literacy, Go controllers, Helm authoring, and GitOps via Flux or ArgoCD.

Strong observability expertise across metrics, logs, and traces, with the ability to instrument, troubleshoot, and debug production systems using Prometheus, OpenTelemetry, Datadog, or Grafana.

• Hands-on experience building or running MCP servers or AI agent tooling, ideally in production environments.
AI-first mindset: an active user of AI coding assistants who brings agentic workflows 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 strong ownership: self-directed and hands-on, comfortable building where no playbook exists yet, and driving clarity through action rather than waiting for it.

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