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# Senior Forward Deployed Engineer - Kubernetes

- **Company:** STACKLOK, INC.
- **Location:** Boston, MA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $189,000.0 - $231,000.0
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Software Debugging, Role-Based Access Control, Prometheus, Management of Software Versions, Datadog, Grafana, Kubernetes, Production Code, Virtual Agents
- **Published:** July 9, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=8385668db873250f

## About the Role

Strong software engineering fundamentals: writes well-structured production code with sound design and testing judgment.
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.

## Description

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., 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 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.


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