Description

You will design, build, and manage automated infrastructure supporting AI/ML workloads.

This role is hybrid.

Responsibilities

• Design, implement, and maintain CI/CD pipelines for application and AI/ML model deployments.
• Build and manage cloud-native infrastructure for AI/ML training, testing, and inference workloads.
• Automate infrastructure provisioning using Infrastructure as Code.
• Collaborate with Data Scientists and ML Engineers to productionize ML models (MLOps).
• Monitor, optimize, and troubleshoot system performance, availability, and scalability.

Required Skills

• 12+ years of overall IT experience with a strong focus on DevOps engineering.
• Hands-on experience with CI/CD tools: Jenkins, GitHub Actions, GitLab CI, or Azure DevOps.
• Strong expertise in AWS, Azure, or GCP, with preference for AI/ML services.
• Experience supporting AI/ML pipelines and MLOps frameworks (MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI).
• Expertise in Docker, Kubernetes, and microservices architecture.
• Strong scripting skills in Python, Bash, or PowerShell.
• Experience with monitoring and logging tools (Prometheus, Grafana, ELK, CloudWatch, Azure Monitor).
• Proficiency with Terraform and CloudFormation.
• Solid understanding of Linux systems, networking, and security best practices.

Key Skills

Education

Any Graduate