Description

You will build and maintain the infrastructure supporting production AI/ML workloads.

This role is on-site.

Responsibilities

• Design, build, and maintain ML pipelines for training, testing, and deploying AI/ML models.
• Manage and optimize cloud-based ML infrastructure using GCP Vertex AI, AWS SageMaker, or similar services.
• Develop and manage CI/CD pipelines for AI/ML applications ensuring automated deployment and version control.
• Implement observability and monitoring for model performance, system health, and resource utilization.
• Automate end-to-end workflows for data ingestion, model training, deployment, and monitoring.

Required Skills

• 6+ years of experience in DevOps, CloudOps, or MLOps roles.
• Strong experience with GCP AI/ML services (Vertex AI, BigQuery ML, AI Platform) or AWS ML stack (SageMaker).
• Hands-on experience with containerization and orchestration tools – Docker, Kubernetes.
• Proficiency in Infrastructure-as-Code tools – Terraform, CloudFormation, or Deployment Manager.
• Expertise in CI/CD pipelines using Jenkins, GitLab CI, GitHub Actions, or ArgoCD.
• Programming experience in Python, Bash, or Go, including ML frameworks such as TensorFlow, PyTorch, Scikit-learn.
• Strong understanding of DevOps automation, security, and scalability in AI/ML production systems.

Key Skills

Education

Any Graduate