MLOps Engineer

eazygurus; Hyderabad; On-site; INR 900,000 - 1,200,000; Full time

Benefits offered by this job

Production ML projects

Collaboration with AI teams

Career growth opportunities

Job summary

eazygurus is seeking an experienced MLOps Engineer in Hyderabad to design, deploy, and maintain scalable ML systems. You will work closely with data scientists and engineering teams to operationalize models and ensure reliability in production.

Responsibilities include building end-to-end ML pipelines, deploying models, implementing CI/CD for ML workflows, and monitoring performance and data drift. Strong Python expertise and hands-on cloud/container experience are required.

Qualifications

• Strong Python programming ability and hands-on ML experience.
• Experience deploying ML models in production and managing CI/CD pipelines.
• Knowledge of cloud platforms (AWS/Azure/GCP) and container orchestration (Docker/Kubernetes).
• Familiarity with ML pipelines, data drift monitoring, and model lifecycle management.

Responsibilities

• Design, build, and manage end-to-end ML pipelines.
• Deploy and maintain ML models in production environments.
• Implement CI/CD pipelines for ML workflows and automate training/deployment.
• Monitor model performance, data drift, and system health; ensure scalability and reliability.
• Collaborate with data science and engineering teams to improve ML delivery.

Skills

Python

TensorFlow

PyTorch

Scikit-learn

Docker

Kubernetes

CI/CD

AWS/Azure/GCP

ML lifecycle

Monitoring

Job description

Location: Hyderabad

Experience: 2–5 Years

Employment Type: Full-time

About the Role

We are looking for an experienced MLOps Engineer to build, deploy, and maintain scalable machine learning systems. You will work closely with data scientists and engineering teams to operationalize ML models and ensure reliability in production.

Key Responsibilities

• Design, build, and manage end-to-end ML pipelines
• Deploy and maintain machine learning models in production
• Implement CI/CD pipelines for ML workflows
• Automate model training, versioning, and deployment processes
• Monitor model performance, data drift, and system health
• Manage containerized deployments using Docker and Kubernetes
• Collaborate with cross-functional teams to improve ML delivery
• Ensure best practices in scalability, security, and reliability

Required Skills & Qualifications

• 2–5 years of experience in MLOps, DevOps, or ML Engineering
• Strong proficiency in Python
• Hands-on experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
• Experience with Docker and Kubernetes
• Knowledge of CI/CD tools (Jenkins, GitHub Actions, GitLab CI, etc.)
• Experience with cloud platforms (AWS, Azure, or GCP)
• Understanding of ML lifecycle, deployment, and monitoring

Good to Have

• Experience with MLflow, Kubeflow, Airflow, or similar tools
• Knowledge of data pipelines and ETL processes
• Familiarity with infrastructure as code (Terraform, CloudFormation)
• Experience with monitoring tools and logging frameworks

What We Offer

• Work on real-world, production ML systems
• Opportunity to collaborate with AI and data science teams
• Continuous learning and career growth opportunities