Cloud Orchestration & Scheduling Architect (AI Infrastructure)

Description

We are seeking a Cloud Orchestration & Scheduling Architect to design and implement intelligent systems that dynamically balance AI workload demand, supply and compute capacity across cloud data centers. The role focuses on building orchestration, scheduling, and optimization frameworks for large-scale AI inference workloads, ensuring efficient use of compute, energy, and cost resources.

• Continuously evaluate new frameworks and optimization algorithms to enhance system scalability and resilience.
• Optimize resource allocation by balancing compute demand, supply and economics.
• Build automation pipelines for dynamic scaling and job scheduling.
• Design the orchestration layer for AI workloads across distributed data centers.
• Build dashboards and observability tools to track compute utilization, cost efficiency.
• Design and implement workload orchestration and scheduling systems for AI inference pipelines across distributed data centers.
• Integrate predictive demand forecasting and energy availability data into scheduling decisions.
• Manage multi-region orchestration using Kubernetes, Ray, or similar distributed compute frameworks.
• Implement cost-aware, and latency-aware scheduling policies for optimal workload placement.
• Shape a first-of-its-kind AI + clean energy platform.
• Build automation to dynamically scale GPU/CPU clusters based on real-time and forecasted AI inference workloads.
• Monitor system performance and drive continuous improvements in utilization and efficiency.
• Develop mechanisms to match workload demand with available compute and supply, optimizing for performance, cost, and sustainability.

Requirements

The ideal candidate will have strong experience in Kubernetes, distributed systems, and scheduling frameworks, with a deep understanding of how to align AI compute workloads with real-time demand and supply.

• Understanding of GPU utilization optimization, and CUDA profiling for efficient model execution.
• Knowledge of GPU orchestration, NVIDIA Triton, or KServe for model serving at scale.
• Experience with inference batching, request routing, and autoscaling strategies using frameworks like Ray Serve, Triton Inference Server, or KServe.
• Experience in AIOps: automated monitoring, anomaly detection, root-cause analysis, and predictive operations for AI workloads.
• Strong systems thinking and the ability to design control mechanisms for complex, distributed workloads.
• Strong proficiency in Kubernetes, EKS, or Ray for distributed workload orchestration.
• Startup DNA → bias to action, comfort with ambiguity, love for fast iteration, and flexible and growth mindset.
• Understanding scheduling algorithms, cluster autoscaling, and load balancing in large systems.
• Hands-on experience managing workloads on AWS, or hybrid cloud platforms.
• Hands-on experience with container orchestration, automation, and observability.
• Proactive, ownership-driven approach, with the ability to improve systems end-to-end.
• Agile mindset, adaptability, and eagerness to learn emerging tools and technologies.
• Experience building or tuning custom schedulers for optimization.
• Proficiency in Python, Go, or Bash scripting for automation.
• Exposure to monitoring and observability tools (Prometheus, Grafana, CloudWatch).
• Proven experience in AI/ML infrastructure orchestration, workflow scheduling, or HPC environments.
• Certifications (preferred):
• Experience building demand forecasting and queue-based scheduling (Redis, Kafka, or similar) systems to balance compute load and supply.
• Familiarity with containerization (Docker), IaC tools (Terraform/CloudFormation), and GitOps (Argo CD).
• Bachelor's or master's degree in computer science, Information Technology, or related field.
• Proven experience of 2–5 years in cloud infrastructure, distributed systems, or large-scale DevOps environments.
• Certified Kubernetes Administrator (CKA)
• Curiosity about distributed scheduling and cloud optimization.
• Experience with workflow orchestration tools like Airflow, Argo, or Prefect.

Benefits

• Work with a small, mission-driven team obsessed with impact.
• A chance to leave your mark at the intersection of AI and sustainability.

Training + Development

Interview process

Visa Sponsorship

Security clearance