Cloud Orchestration

Location: Bengaluru, Karnataka

About the Company:

Sustainability is a global organization, pioneering the convergence of clean energy and AI, enabling profitable energy transitions while powering end-to-end AI infrastructure. By integrating AI-driven cloud solutions with sustainable energy, we create scalable, intelligent ecosystems that drive efficiency, innovation, and long-term impact across industries. Guided by exceptional leaders and visionaries with decades of expertise in finance, policy, technology, and innovation, we are committed to making long-term efforts to fulfil this vision through our technical innovation, client services, expertise, and capability expansion. 

Role Summary:

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.

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.

Key Responsibilities:

• Design and implement 
  workload orchestration and scheduling systems
   for AI inference pipelines across distributed data centers.
• Develop mechanisms to 
  match workload demand with available compute and supply
  , optimizing for performance, cost, and sustainability.
• Integrate 
  predictive demand forecasting
   and 
  energy availability data
   into scheduling decisions.
• Manage 
  multi-region orchestration
   using Kubernetes, Ray, or similar distributed compute frameworks.
• Build automation to dynamically 
  scale GPU/CPU clusters
   based on real-time and forecasted AI inference workloads.
• Implement 
  cost-aware, and latency-aware scheduling policies
   for optimal workload placement.
• Build dashboards and observability tools to track 
  compute utilization, cost efficiency.
• Continuously evaluate new frameworks and optimization algorithms to enhance system scalability and resilience.

Education & Experience:

• 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.
• Proven experience in AI/ML infrastructure orchestration, workflow scheduling, or HPC environments.
• Hands-on experience managing workloads on AWS, or hybrid cloud platforms.
• Certifications
   (preferred):
• AWS Certified Solutions Architect – Associate
• Certified Kubernetes Administrator (CKA)

Skills Required:

• Strong proficiency in 
  Kubernetes
  , 
  EKS
  , or 
  Ray
   for distributed workload orchestration.
• Experience with 
  workflow orchestration tools
   like Airflow, Argo, or Prefect.
• Familiarity with containerization 
  (Docker), IaC tools (Terraform/CloudFormation)
  , and 
  GitOps (Argo CD)
  .
• Understanding 
  scheduling algorithms
  , 
  cluster autoscaling
  , and 
  load balancing
   in large systems.
• Experience with inference batching, request routing, and autoscaling strategies using frameworks like 
  Ray Serve
  , 
  Triton Inference Server
  , or 
  KServe
  .
• Experience building or tuning 
  custom schedulers
   for optimization.
• Knowledge of 
  GPU orchestration
  , 
  NVIDIA Triton
  , or 
  KServe
   for model serving at scale.
• Understanding of 
  GPU utilization optimization
  , and 
  CUDA profiling
   for efficient model execution.
• Experience in AIOps
  : automated monitoring, anomaly detection, root-cause analysis, and predictive operations for AI workloads.
• Experience building or tuning 
  custom schedulers
   for optimization.
• Experience building 
  demand forecasting
   and 
  queue-based scheduling
   (Redis, Kafka, or similar) systems to balance compute load and supply.
• Proficiency in Python, Go, or Bash scripting for automation.
• Exposure to monitoring and observability tools (Prometheus, Grafana, CloudWatch).

What You'll Do:

• Design the orchestration layer for AI workloads across distributed data centers.
• Optimize resource allocation by balancing compute demand, supply and economics.
• Build automation pipelines for dynamic scaling and job scheduling.
• Monitor system performance and drive continuous improvements in utilization and efficiency.

What you will bring

• Strong systems thinking and the ability to design control mechanisms for complex, distributed workloads.
• Passion for optimizing AI infrastructure for efficiency, reliability, and sustainability.
• Curiosity about 
  distributed scheduling
   and 
  cloud optimization.
• Hands-on experience with container orchestration, automation, and observability.
• A proactive, ownership-driven mindset suited for a fast-paced, mission-driven environment.
• Agile mindset, adaptability, and eagerness to learn emerging tools and technologies.
• Proactive, ownership-driven approach, with the ability to improve systems end-to-end.
• Startup DNA → bias to action, comfort with ambiguity, love for fast iteration, and flexible and growth mindset.

Why Join Us

• Shape a 
  first-of-its-kind AI + clean energy platform

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

.