MLOps Engineer (2-9 Years)

Infosys; Hyderabad, Pune District, Bengaluru; Hybrid; INR 1,500,000 - 2,500,000; Full time

Job summary

Infosys is seeking an experienced MLOps/DevOps/CloudOps Engineer to design, automate, deploy, and maintain scalable cloud-native applications and ML platforms. The role covers end-to-end ML pipelines, CI/CD, infrastructure automation, and multi-cloud operations.

The candidate will collaborate with Data Scientists, Data Engineers, and Platform teams to implement monitoring, drift detection, and security controls across AWS, Azure, or GCP environments.

Qualifications

• 2–9 years of experience in MLOps/DevOps/CloudOps.
• Experience designing, automating, deploying, and maintaining scalable cloud-native apps and ML platforms.
• Strong expertise in cloud technologies, CI/CD pipelines, infrastructure automation, containerization, and monitoring.
• Experience across multi-cloud environments.

Responsibilities

• Build and maintain end-to-end ML pipelines for deployment and monitoring.
• Automate ML workflows using MLOps best practices.
• Deploy and manage ML models in production.
• Design, build, and maintain CI/CD pipelines.
• Manage infrastructure provisioning and configuration.
• Implement DevSecOps practices and security controls.
• Monitor cloud resources for performance, cost, and availability.

Skills

MLOps

DevOps

CloudOps

CI/CD

Infrastructure as code

Containerization

Monitoring

Multi-cloud

Security (DevSecOps)

Tools

AWS

Azure

GCP

Kubernetes

CI/CD tooling

Job description

Position: MLOps / DevOps / CloudOps Engineer
Experience: 2-9 Years
Location: PAN India
Employment Type: Full-Time

Job Summary

We are looking for talented MLOps / DevOps / CloudOps Engineers with 2-9 years of experience to design, automate, deploy, and maintain scalable cloud-native applications and machine learning platforms. The ideal candidate should have expertise in cloud technologies, CI/CD pipelines, infrastructure automation, containerization, monitoring, and operational excellence across multi-cloud environments.

Key Responsibilities

MLOps

• Build and maintain end-to-end ML pipelines for model development, deployment, monitoring, and retraining.
• Automate machine learning workflows using MLOps best practices.
• Deploy and manage ML models in production environments.
• Collaborate with Data Scientists, Data Engineers, and Platform teams.
• Implement model monitoring, performance tracking, and drift detection mechanisms.

DevOps

• Design, build, and maintain CI/CD pipelines.
• Automate infrastructure provisioning and configuration management.
• Manage code repositories, branching strategies, and release processes.
• Implement DevSecOps practices and security controls.
• Improve deployment efficiency, reliability, and scalability.

CloudOps

• Manage and optimize cloud infrastructure across AWS, Azure, or GCP.
• Monitor cloud resources, performance, cost, and availability.
• Troubleshoot infrastructure and application issues.
• Ensure high availability, disaster recovery, and business continuity.
• Implement infrastructure-as-code and cloud governance standards.