MLOps Engineer

EAZYGURUS IT TRAINING; Hyderabad; On-site; INR 1,100,000 - 1,700,000; Full time

Job summary

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

The role emphasizes end-to-end ML pipelines, CI/CD for ML, containerized deployments with Docker and Kubernetes, and monitoring data drift and system health in cloud environments.

Qualifications

• 2–5 years of experience in MLOps, DevOps, or ML Engineering.
• Strong proficiency in Python.
• Hands-on experience with ML frameworks: TensorFlow, PyTorch, and 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.

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

Skills

Python

TensorFlow

PyTorch

Scikit-learn

Docker

Kubernetes

CI/CD tools

Cloud platforms

ML lifecycle

Tools

Docker

Kubernetes

Jenkins

GitHub Actions

GitLab CI

Job description

About The Role

We are looking for an experienced

Location: Hyderabad

Experience: 2–5 Years

Employment Type: Full-time

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