MLOps Engineer

Overview

Machine Learning Ops (MLOps) Engineer role to design, deploy, and maintain scalable ML systems in production.

What you'll do

• Design, implement, and maintain ML pipelines for training, validation, and deployment.
• Automate ML training, testing, and deployment using CI/CD for ML models.
• Monitor model performance, data drift, and system health in production.
• Deploy models in pre-prod and production environments.
• Collaborate with data scientists to operationalize ML models and algorithms.
• Optimize ML infrastructure for scalability, reliability, and cost-effectiveness.
• Maintain documentation for ML workflows, deployment processes, and system architecture.

What you'll need

• 3–5 years of experience in software development, DevOps, or data engineering.
• Proficiency in Python and SQL.
• Hands-on experience with at least one ML framework (TensorFlow, PyTorch, or Scikit-learn).
• Containerization experience with Docker and orchestration with Kubernetes.
• Cloud platform experience with AWS, Azure, or GCP.
• Knowledge of CI/CD pipelines and Git-based version control for models and datasets.
• Familiarity with monitoring/logging and data pipeline tools such as Apache Airflow or Kubeflow.

Details

• Location: Bengaluru, India.
• May require availability outside standard business hours for a rotational on-call schedule.