Cloud/DevOps Engineer

Job Title: Senior DevOps Engineer – AI/ML (12+ Years Experience)

Job Summary

We are looking for a highly experienced Senior DevOps Engineer with strong AI/ML exposure to design, build, and manage scalable, secure, and automated infrastructure supporting AI/ML workloads. The ideal candidate will have 12+ years of IT experience, deep DevOps expertise, and hands-on experience enabling machine learning and AI platforms in cloud environments.

Key Responsibilities

• Design, implement, and maintain CI/CD pipelines for application and AI/ML model deployments.
• Build and manage cloud-native infrastructure to support AI/ML training, testing, and inference workloads.
• Automate infrastructure provisioning using Infrastructure as Code (Terraform, CloudFormation, ARM).
• Collaborate with Data Scientists and ML Engineers to productionize ML models (MLOps).
• Implement and manage containerization and orchestration using Docker and Kubernetes.
• Monitor, optimize, and troubleshoot system performance, availability, and scalability.
• Ensure security, compliance, and governance across DevOps and AI/ML platforms.
• Manage model versioning, data pipelines, and deployment workflows.
• Drive DevOps best practices and mentor junior engineers.

Required Skills & Qualifications

• 12+ years of overall IT experience with strong focus on DevOps engineering.
• Proven experience in DevOps tools: Jenkins, GitHub Actions, GitLab CI, Azure DevOps, etc.
• Strong hands-on experience with Cloud Platforms: AWS / Azure / Google Cloud Platform (AI/ML services preferred).
• 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).
• Solid understanding of Linux systems, networking, and security best practices.

Nice to Have

• Experience with GenAI, LLM deployments, or GPU-based workloads.
• Knowledge of data engineering pipelines (Airflow, Kafka).
• Certifications in AWS, Azure, Google Cloud Platform, or Kubernetes.

Soft Skills

• Excellent communication and stakeholder collaboration skills.
• Strong problem-solving and leadership abilities.
• Ability to work in fast-paced, enterprise environments.