Senior Forward Deployed Engineer

Stacklok•Bellevue, WA

2d•Hybrid

About The Position

Stacklok is building the control plane for enterprise AI agents, enabling organizations to run, govern, and secure them on the infrastructure they already trust. This is a hybrid role based in the United States that requires in-person work at our Bellevue, Washington office three days per week on Tuesday, Wednesday, and Thursday. As a Senior Forward Deployed Engineer, you will sit where platform engineering meets AI. You will bring deep Kubernetes expertise to help enterprises adopt Stacklok's Enterprise platform and run AI agents securely on the infrastructure they already trust. This is hands-on, high-ownership work. You will lead full forward-deployed engagements end to end, stand up custom proof-of-concepts with Design Partners, and answer the hard technical questions that come up as customers move toward production. When unlocking value means changing how the product deploys to Kubernetes, you will own that work and contribute the changes back to Stacklok Enterprise itself. Demand for this work is outpacing the team's capacity, so you will have a front-row seat to how leading enterprises put AI into production, and real influence over what gets built next. If you want your platform expertise to land directly with customers, this is your opportunity to do it at the frontier of enterprise AI.

Requirements

Deep Kubernetes expertise across cluster architecture, workload types, networking, storage, RBAC, and resource management, with the ability to design and debug permission models.
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.
Proven experience deploying into managed Kubernetes, fluent in how it meets cloud infrastructure (IAM, networking, storage), with Helm authoring and GitOps via Flux or ArgoCD.
Comfortable working directly with customers, including over-the-shoulder debugging across varied enterprise permission models, and clearly communicating root causes and remediation steps.
Solid grasp of observability across metrics, logs, and traces, and when each applies.
A track record of delivering production-grade systems and contributing fixes back to the products you support.
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.

Nice To Haves

Familiarity with Prometheus, OpenTelemetry, and platforms like Datadog or Grafana is a plus.
Hands-on experience with MCP servers or AI agent tooling is a strong plus.

Responsibilities

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.