Description

You will build and manage the infrastructure and automation required to support the full machine learning lifecycle.

Responsibilities

• Build scalable MLOps frameworks and automated pipelines using Airflow or Kubeflow for data and ML engineering.
• Implement CI/CD practices to automate model deployment, version control, testing, and rollbacks for ML and data platforms.
• Design and architect orchestration services for ML and data engineering components to ensure optimal scaling.
• Provision and manage GCP resources and Kubernetes clusters using automated tools and Helm.
• Develop monitoring and alerting systems to track model performance, data drift, and system health in GCP.

Required Skills

• 8+ years of IT experience with at least 4+ years in DevOps, CloudOps, Dockerization, and Containerization.
• Mandatory experience with Google Cloud Platform (GCP) and Google Kubernetes Engine (GKE).
• Proficiency with Kubernetes, Helm manifest management, and Dockerization of ML scripts.
• Hands-on experience with CI/CD tools such as Jenkins, CircleCI, or Google Cloud Build.
• Experience with infrastructure provisioning using Terraform or Google Deployment Manager.
• Strong programming skills in Python, Go, and Bash.
• Knowledge of experiment tracking, model registries, data versioning, and production drift monitoring.
• Experience with distributed computing, data warehousing, and big data technologies.
• Technical proficiency in JIRA, DNS, Linux system configuration, Tomcat, and JBoss.

Preferred Skills

• Familiarity with Agile/Scrum processes and JIRA for DevOps task management.

Key Skills

Education

Any Graduate