Sr. Director - Backend Engineering

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Sr Director- Backend Engineering

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

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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.

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

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

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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

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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).

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

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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

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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).

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

Qualifications

Required

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

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Experience:

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

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

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

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

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

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

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

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

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

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

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Skills:

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

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Software Defined Cloud Networking.

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Strong background in DevOps and MLOps principles and tooling.

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

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

Preferred

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

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Experience in a regulated industry with strict compliance requirements.

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AI Private Cloud - Building and operating.

Success Metrics

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

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

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

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The reliability and uptime of the core AI platform services.

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The talent retention and development within the AI Infrastructure team.