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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in leading engineering teams focused on Kubernetes platform infrastructure and cloud systems, with a strong emphasis on operational quality, reliability, and strategic planning. Proficient in leveraging AI to enhance workflows and decision-making while maintaining accountability and data integrity.
Highest-signal resume keywords
Kubernetes Platform ManagementCloud Infrastructure EngineeringTeam Leadership and DevelopmentAI Utilization for Workflow ImprovementOperational Quality Assurance
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
KubernetesCloud SystemsInfrastructure as CodeDistributed SystemsProduction OperationsSite Reliability EngineeringTechnical Strategy DevelopmentAutomationObservabilityNetworking
Soft Skills
High IntegrityOwnershipAccountabilityCoachingCollaboration
Tools & Technologies
Amazon EKSGoogle Kubernetes Engine (GKE)AI ToolsSelf-Service PlatformsIncident Response Systems
Certifications & Qualifications
Bachelor’s Degree in Computer Science or Related Field
Industry Keywords
Production EngineeringCloud Cost ManagementResource AllocationCapacity ManagementMulti-Tenant Compute Platforms
Tech Stack
Tools & technologiesCloudDistributed SystemsKubernetes
About the role
Key responsibilities & impact- Lead a team of engineers responsible for Kubernetes platform infrastructure and supporting systems
- Establish the team’s 12-month strategy, roadmap, and success measures
- Improve platform reliability, security, scalability, and operational efficiency
- Drive architecture and technical decisions, including in unfamiliar areas
- Build automation, standard configurations, and self-service paths to reduce manual work
- Set operational standards for service health, observability, on-call, incident response, and safe changes
- Partner with engineering, product, finance, security, and cloud-provider teams
- Recruit, coach, and develop engineers; empower senior technical leaders; and sustain an inclusive, accountable team
- Use AI to accelerate analysis, investigations, documentation, and repeatable workflows while verifying quality
Requirements
What you’ll need- Bachelor’s degree in Computer Science, a related field, or equivalent practical experience
- Minimum 3 years of experience managing engineering teams in infrastructure, platform engineering, Site Reliability Engineering, cloud systems, distributed systems, or similar technical areas
- Experience managing and developing teams that build or operate large-scale cloud infrastructure, Kubernetes platforms, shared compute platforms, or other production systems
- Experience setting a multi-quarter technical strategy and delivering through senior engineers
- Technical experience in public cloud, highly available distributed systems, Kubernetes or shared-compute platforms, and production operations
- Experience leading teams responsible for the operational quality of production systems
- Experience leading cross-functional infrastructure programs and communicating reliability, security, cost, capacity, and delivery tradeoffs
- Demonstrated ability to use AI to improve speed and quality in day-to-day work
- Strong record of evaluating AI-assisted work through testing, source checking, data validation, or peer review
- High integrity and ownership, including protecting sensitive data, avoiding over-reliance on AI, and remaining accountable for final decisions and deliverables
- Preferred: experience managing Production Engineering, Site Reliability Engineering, or similarly production-focused infrastructure teams
- Preferred: experience with managed Kubernetes services, especially Amazon EKS or Google Kubernetes Engine (GKE)
- Preferred: experience operating a large Kubernetes fleet or multi-tenant compute platform
- Preferred: experience with infrastructure as code, Kubernetes lifecycle management, observability, networking, or production upgrades
- Preferred: experience with GPU or machine-learning infrastructure, capacity management, cloud cost, or resource allocation
Benefits
Comp & perks- Equity eligibility
- Flexible work model
- In-person collaboration 1–2 times per quarter
- Equal opportunity employment
- Medical or religious accommodation support