Director of AI Infrastructure & Platform Operations

About the company Our hiring partner builds AI cloud infrastructure and software that make GPU compute more accessible, efficient and reliable. The platform supports demanding AI workloads through GPU orchestration, containerized services, resource scheduling and distributed systems. About the role We are seeking a Director of AI Infrastructure & Platform Operations to own infrastructure and platform operations while building and developing the teams behind them. This is a technical leadership role spanning platform engineering, DevOps, site reliability and MLOps, not a general business operations position. You will connect people, processes and technology to deliver reliable services, efficient AI compute and a strong developer and customer experience. Location and work arrangement Seattle, Washington, United States. This is a hybrid role requiring regular in-person collaboration in Seattle; it is not a fully remote position. Key responsibilities

• Own the operating strategy, roadmap and day-to-day health of infrastructure and platform services supporting AI workloads.
• Build, hire, coach and develop infrastructure, platform, DevOps and MLOps teams; establish clear ownership, performance expectations and career-development practices.
• Partner with engineering and product leaders to translate business priorities into operational capacity, reliable releases and measurable service outcomes.
• Lead production operations for Linux and Kubernetes-based environments, including provisioning, capacity planning, scaling, change management and recovery.
• Establish service-level objectives, actionable observability, on-call practices, incident response, post-incident reviews and continuous reliability improvements.
• Drive DevOps automation through CI/CD, infrastructure as code and repeatable deployment processes that improve delivery speed without compromising stability.
• Own the operational foundations for ML workloads, partnering with AI engineers on reproducible deployments, model-serving reliability, monitoring and rollback across training or inference environments.
• Improve GPU resource utilization, workload scheduling and multi-tenant service quality while balancing performance, availability and infrastructure cost.
• Evaluate opportunities to improve operations through AIOps, including anomaly detection, alert correlation and safely controlled remediation where appropriate.
• Coordinate security, access controls, operational risk management and business continuity with relevant engineering and infrastructure stakeholders.
• Report operational health, capacity, incident trends and cost-efficiency metrics clearly to technical and executive stakeholders.

Required qualifications

• Senior-level experience owning production infrastructure or platform operations, with demonstrated accountability for reliability, scalability and service delivery.
• Direct people-management experience, including hiring, coaching, performance management and developing technical teams.
• Demonstrated DevOps and MLOps experience in production, with concrete examples of improving deployment workflows, operational reliability or ML platform delivery.
• Strong technical understanding of cloud infrastructure, platform engineering, Linux, Kubernetes and containerized services.
• Experience leading CI/CD, infrastructure-as-code and observability practices, and making sound trade-offs across speed, security, cost and reliability.
• A track record of leading incident response and driving durable improvements rather than relying on repeated manual intervention.
• Ability to collaborate with software, AI/ML and product teams and communicate priorities and technical trade-offs to executives.
• Comfort operating in a fast-moving environment, creating structure and clear accountability as teams and systems scale.
• Applicants must currently reside in the United States, be available for hybrid work in Seattle, and be authorized to work in the United States without employer visa sponsorship.

Nice to have

• Experience with GPU infrastructure, HPC environments, AI compute scheduling or multi-tenant distributed systems.
• Prior ownership of AIOps initiatives with demonstrated reductions in alert noise, incident duration or repetitive operational work.
• Familiarity with Prometheus, Grafana, Docker or similar tools used in cloud-native environments.
• Experience with AWS or Google Cloud Platform; experience with both providers is not required.
• Working familiarity with Go or Python to collaborate effectively with platform engineers; expertise in both languages is not required.