Sr. Manager, AI Platform Engineering

Job summary

Leads the architecture and evolution of large-scale infrastructure supporting software and AI model development, deployment, and operations. The role sets infrastructure strategy, mentors engineering teams, and drives automation, performance optimization, security, capacity planning, and cost management across GPU clusters, high-performance computing environments, and hybrid cloud platforms. Key technologies include Linux, Python, NVIDIA GPUs, CUDA, Kubernetes, and infrastructure-as-code tools.

Qualifications

• Bachelor’s or master’s degree in computer engineering, electrical engineering, computer science, or a related field; advanced degree preferred.
• At least 12 years of relevant software, infrastructure, DevOps, or SRE experience, including at least 4 years focused on AI/ML infrastructure.
• Experience leading or mentoring engineering teams and leading 24/7 managed services and operations teams.
• Deep expertise in computer systems, distributed systems, and cloud computing architectures.
• Expert Linux system administration skills, including shell scripting, networking, system configuration, and kernel tuning.
• Strong Python proficiency and proficiency in at least one additional systems language.
• Experience with AI hardware and software stacks, including NVIDIA GPUs, CUDA, cuDNN, NCCL, and InfiniBand or RoCE.
• Experience managing high-performance computing environments, including scheduling, resource allocation, cluster maintenance, and performance tuning.
• Hands-on experience with major cloud platforms and hybrid cloud architectures.
• Advanced experience with Docker and Kubernetes, including custom operators, Helm charts, and GPU scheduling plugins.
• Experience with observability tools such as Prometheus, Grafana, ELK/OpenSearch, or Datadog, and infrastructure-as-code tools such as Terraform, Ansible, or Pulumi.
• Strong architectural judgment, communication, collaboration, and technical leadership skills.

Responsibilities

• Define infrastructure strategy and architect scalable, resilient, and cost-efficient compute, storage, and networking for the AI lifecycle.
• Lead, manage, and mentor platform and infrastructure engineers; establish engineering standards and conduct design reviews.
• Build and maintain automation pipelines for software development and AI model training, evaluation, testing, and deployment.
• Collaborate with data scientists, machine learning engineers, product managers, and leadership on infrastructure requirements.
• Profile, benchmark, and optimize AI infrastructure and model serving for throughput, latency, GPU utilization, scalability, and cost.
• Establish security practices, access controls, and compliance frameworks for AI infrastructure.
• Forecast capacity, plan resources, and optimize costs for GPU clusters and associated infrastructure.

Skills

Relocation