Principal DevOps Engineer (AI Enablement)

Key Responsibilities

• Technical direction & architecture: Own the architecture for CI/CD, infrastructure, and the AI delivery platform; set the technical strategy and reference architectures that other teams build on.
• Developer experience (DevEx): Own developer experience as a long-term, principal-level charter — continuously improve how engineers build, test, and ship, and treat DevEx as a product with its own metrics and roadmap.
• Standards & paved roads: Define and champion reusable templates, golden paths, and engineering standards that make delivery fast, safe, and self-service — using AI tooling to raise the bar on those standards, not just to move faster.
• AI in the daily workflow: Set the example and the patterns for using AI coding tools (e.g., Claude, Cursor) day to day, and turn that hands-on use into improved practices, reviews, and automation across the team — with disciplined, cost-aware token usage rather than wasteful consumption.
• CICD delivery: Lead the design of continuous integration and deployment pipelines with automated testing, validation, and rollback.
• Infrastructure as code: Establish IaC and GitOps practices (Terraform or equivalent) across one or more major clouds.
• Scales AI tooling & AIOps: Drive org-wide adoption of AI-assisted review, test generation, and AIOps-driven incident detection and remediation, and quantify their impact on quality, velocity, and reliability.
• Observability & AIOps: Define observability strategy — telemetry, logging, automated anomaly detection, and alerting — and apply AIOps techniques to set SLOs that reduce mean-time-to-resolution.
• DevSecOps & responsible AI use: Embed security scanning, secrets management, and sensible guardrails for how AI tooling is used (e.g., data handling, access, and review practices) across the delivery lifecycle.
• Reliability & cost: Set reliability practices (SLOs, DORA metrics) and optimize infrastructure and tooling cost — including efficient, token-aware use of AI tools.
• Mentorship & influence: Mentor and uplevel engineers, lead technical reviews, and influence the broader engineering roadmap and ways of working.

Required Qualifications

• MUST be located in the Greater Seattle area and be willing to work onsite.
• 8+ years in DevOps, platform engineering, or site reliability engineering, including a track record of senior technical ownership.
• Demonstrated experience architecting and setting direction for CI/CD and infrastructure platforms used across multiple teams.
• Strong CI/CD experience (e.g., GitHub Actions, GitLab CI, Jenkins, or Azure DevOps).
• Deep proficiency with infrastructure-as-code (Terraform, Pulumi, or CloudFormation) and at least one major cloud (AWS, Azure, or GCP).
• Hands-on containerization and orchestration with Docker and Kubernetes, including production-scale operations.
• Strong scripting and automation skills in Python, Bash, or Go.
• Experience defining observability and monitoring strategy (e.g., Prometheus, Grafana, Datadog, ELK).
• Regular, hands-on use of AI to make DevOps work more efficient — automating tasks, generating and reviewing code, troubleshooting, and improving tooling.
• Daily, hands-on use of AI coding tools (e.g., Claude, Cursor, or equivalent) as a core part of how they work — with a cost-conscious, token-aware approach rather than wasteful consumption.
• A demonstrated track record of using AI to make engineering standards and practices measurably better (e.g., better reviews, tests, automation, or paved-road tooling), not just to ship faster.
• Some level of AIOps experience — using AI-driven techniques for monitoring, anomaly detection, alerting, or automated remediation.
• Genuine interest in and ownership instinct for developer experience (DevEx) as a long-term concern.
• Proven ability to mentor engineers, lead technical decisions, and influence across teams.

Preferred Qualifications

• People or team management experience — leading, growing, or formally managing a DevOps / platform / DevEx team.
• A strong plus for candidates on a player-coach or principal-with-reports path, but not required.
• Experience standing up or owning a platform or developer-experience function from the ground up.
• Experience evaluating, rolling out, and measuring AI developer tools (e.g., Claude, Cursor, Copilot) across a team or organization.
• Familiarity with AIOps platforms and automated remediation.
• Cost / FinOps discipline for cloud and AI tooling — monitoring and optimizing spend, including token usage.
• Awareness of responsible / secure AI-use practices and relevant data-handling requirements.
• Relevant certifications (CKA/CKAD, cloud architect or DevOps engineer, Terraform Associate).
• Exposure to enterprise ITSM/ITOM platforms (e.g., ServiceNow) and large-scale program environments.