Key Skills and Role Responsibilities:

This role is for a strategic and technical leader to define, build, and operate the infrastructure orchestration systems that power our organization's cutting-edge Artificial Intelligence (AI) initiatives. The Senior Director will lead a team responsible for ensuring a robust, scalable, cost-efficient, and high-performance platform for all stages of the AI lifecycle, from experimentation and training to deployment and inference.

Strategy and Leadership

• Define and execute the long-term vision and roadmap for the company’s AI infrastructure Network Services, aligning it with overall business and AI Services goals.

• Lead, mentor, and grow a high-performing engineering and operations team focused on AI infrastructure and platform engineering.

• Manage budget and resource allocation for AI infrastructure Network Services deliverables.

• Act as a key liaison between AI infrastructure and other services owners and consumers, core engineering, Cloud infrastructure, and executive leadership.

AI Infra Development and Operations

• Oversee the design, implementation, and maintenance of the core network orchestration platforms for large-scale AI model training (e.g., distributed training, hyperparameter tuning) and deployment (e.g., containerization, serverless functions, edge deployment).

• Ensure reliability, security, and compliance of the AI infrastructure, meeting strict standards for data governance and model integrity.

• Establish Service Level Objectives (SLOs) and Key Performance Indicators (KPIs) for the AI platform services and lead efforts for continuous optimization and performance tuning.

Technology and Architecture

• Select, evaluate, and integrate the core technologies required for the AI stack (e.g., Cloud Overlay/Under networking, Infiniband, Load Balancer, DNS, Core Networking, Kubernetes, Ray, GPU/accelerator management, distributed file systems).

• Champion infrastructure-as-code (IaC) principles to manage and provision AI resources consistently and at scale.

Qualifications

Required

• Education: Bachelor's or Master’s degree in Computer Science, Engineering, or a related technical field.

• Experience:

• 15+ years of progressive experience in software engineering, infrastructure, or platform operations.

• 5+ years of experience leading and managing technical teams, ideally in a Director or Sr. Director level or equivalent capacity.

• Deep, hands-on experience designing and operating large-scale distributed systems and cloud-native network architectures.

• Proven experience specifically with AI infrastructure orchestration (e.g., using Kubernetes) and managing accelerated compute resources (GPUs, TPUs, etc.).

• 15+ years of Cloud backend engineering, Cloud Design, Deployment, DevOps.

• 15+ years of experience leading system design and architecture leveraging Private Clouds and AWS and/or Azure/GCP.

• 10+ years of demonstrable experience building and operating infrastructure as code, Infra Automation, and comfort with various flavors of Linux.

• 15+ years of experience in building high-performance, highly available, and scalable distributed systems in the cloud.

• 15+ years of experience in building and managing high-performance, highly available, and scalable Hybrid Cloud environments.

• Excellent cross-group collaboration, outstanding verbal and written communication skills.

• Skills:

• Expert-level knowledge of containerization and orchestration (Docker, Kubernetes).

• Software Defined Cloud Networking.

• Strong background in DevOps and MLOps principles and tooling.

• Proficiency in at least one modern programming language (e.g., Python, Go).

• Exceptional strategic planning, organizational, and written/verbal communication skills.

Preferred

• Prior experience managing infrastructure for training and inference of large language models (LLMs) or foundation models.

• Experience in a regulated industry with strict compliance requirements.

• AI Private Cloud - Building and operating.

Success Metrics

A successful Senior Director - AI Infrastructure Orchestration will be measured by:

• The time-to-market for AI infrastructure build, scale, and operation.

• The resource utilization rate and cost efficiency of the AI compute infrastructure.

• The reliability and uptime of the core AI platform services.

• The talent retention and development within the AI Infrastructure team.