MLOps Engineer

eazygurus; Hyderabad; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

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

Responsibilities include designing end-to-end ML pipelines, deploying models, and implementing CI/CD for ML workflows, with a focus on scalability, security, and observability. You will automate training, versioning, and monitoring to minimize drift.

Qualifications

• 2-5 years of experience in MLOps, DevOps, or ML Engineering.
• Strong proficiency in Python.
• Experience with Docker and Kubernetes.
• Experience with cloud platforms (AWS, Azure, or GCP).
• Understanding of ML lifecycle, deployment, and monitoring.

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.
• Collaborate with cross-functional teams to improve ML delivery.
• Ensure best practices in scalability, security, and reliability.

Skills

Python

ML lifecycle

CI/CD concepts

Cloud platforms

Tools

Docker

Kubernetes

TensorFlow

PyTorch

Scikit-learn

Jenkins

GitHub Actions

GitLab CI

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