DevOps/MLOps Engineer || Contract Job || 5+ Years Experience

About Client:

Our Client is a global IT services company headquartered in Southborough, Massachusetts, USA. Founded in 1996, with a revenue of $1.8B, with 35,000+ associates worldwide, specializes in digital engineering, and IT services company helping clients modernize their technology infrastructure, adopt cloud and AI solutions, and accelerate innovation. It partners with major firms in banking, healthcare, telecom, and media.

Our Client is known for combining deep industry expertise with agile development practices, enabling scalable and cost-effective digital transformation. The company operates in over 50 locations across more than 25 countries, has delivery centers in Asia, Europe, and North America and is backed by Baring Private Equity Asia.

Job Title : DevOps / MLOps Engineer

Experience Level : 5 to 10 Years

Job Location : Hyderabad

Budget : 1,30,000 Per Month

Client:Virtusa

Endclient:Google

Job Type : Contract

Work Mode : Hybrid

Notice Period : Immediate Joiners

Client : CMMI Level 5

Experience

5+ years

Job Summary

We are seeking a DevOps/MLOps Engineer to manage cloud infrastructure and ML pipelines for scalable AI agent systems. The role focuses on CI/CD automation, Kubernetes-based deployments, MLOps workflows, and production observability on Google Cloud Platform.

Responsibilities

• Automate CI/CD pipelines for AI agent and model lifecycle
• Manage infrastructure using Terraform , GKE , and Cloud Build
• Implement MLOps pipelines with Vertex AI Pipelines and MLflow
• Deploy monitoring and observability tools (Prometheus, Grafana, Stackdriver )
• Ensure secure service-to-service and agent-to-agent communication
• Monitor model performance, versioning, and drift
• Collaborate on security, privacy, and compliance practices

Requirements

• 5+ years experience in DevOps, Platform, or MLOps roles
• Strong expertise in GCP : Vertex AI, GKE, BigQuery, Pub/Sub
• Hands-on experience with Docker and Kubernetes
• Familiarity with Kubeflow, Airflow , or similar ML pipeline tools
• Experience with Infrastructure as Code and production observability