ML Infrastructure / MLOps Engineer

Experience: 3–7 years | MLOps/ML platform +
Kubernetes

Location: Office – Coimbatore/Bengaluru

Aivar is an AI -first technology partner where cutting -edge technology meets industry expertise to supercharge your projects. Our AI -augmented teams accelerate development, reduce time -to -market, and deliver exceptional code quality. We bring together the best minds in tech to craft scalable, repeatable solutions that drive real momentum for your business.

Own the JARK -Stack integration on
EKS: Ray + KubeRay for distributed compute, Kubeflow Pipelines for workflow
orchestration, MLflow for experiment tracking, JupyterHub for development, and
advanced job schedulers (Kueue, Volcano, Argo) for batch training. Bridge
between data scientists and platform.

• Deploy and optimize Ray + KubeRay for distributed data
  processing and model training across GPU clusters
  
• Build Kubeflow Pipelines for reproducible ML workflows
  — data prep, training, evaluation, deployment with lineage tracking
  
• Configure MLflow for centralized experiment tracking
  and model registry across teams
  
• Implement advanced job scheduling — queue management,
  priority, preemption, gang scheduling via Kueue/Volcano
  
• Build model CI/CD — automated training, evaluation,
  validation, and canary/blue -green deployment to inference endpoints
  
• Create self -service tooling for data scientists —
  cluster provisioning, GPU allocation, experiment templates
  
• Monitor ML workload performance — GPU utilization,
  training throughput, data pipeline efficiency
  

• ML infrastructure / MLOps / ML platform engineering
  (3+ years)
  
• Kubernetes (EKS preferred) — deployments, PVs, RBAC,
  resource management
  
• At least two of: Ray/KubeRay, Kubeflow, MLflow,
  Airflow, Argo Workflows
  
• Distributed training — PyTorch DDP, Horovod,
  DeepSpeed, or Ray Train
  
• Model serving — KServe, Seldon, or custom FastAPI
  serving
  
• GPU scheduling and resource management on Kubernetes
  
• Strong Python engineering — tools and automation, not
  just notebooks
  

Ray/KubeRay, Kubeflow Pipelines,
MLflow, JupyterHub, Argo Workflows, Kueue/Volcano, PyTorch/DeepSpeed, KServe,
Helm, AWS (EKS, S3, EFS, ECR), Prometheus/Grafana

Benefits

• Learn from Experts: Work directly with former AWS leaders and AI pioneers.
  
• Direct Ownership: Lead high -impact "greenfield" projects from concept to global launch.
  
• Modern Tech: Master the latest Generative AI frameworks and cloud -native architectures.
  
• Real -World Impact: Build mission -critical systems used by major global enterprises.
  
• Rapid Growth: Scale your career quickly in a high -speed
  

Diversity and Inclusion