ML Operations Engineer

Overview

ML Operations Engineer building and supporting scalable Machine Learning and MLOps systems for reliable model deployment.

What you'll do

• Design and implement scalable ML infrastructure for model training, deployment, and inference.
• Develop and deploy ML solutions as reusable microservices using APIs for integration.
• Package and deploy ML models using Docker and orchestration platforms such as Kubernetes.
• Monitor production systems for model performance, latency, and drift and support optimizations.
• Build and maintain CI/CD pipelines for ML workflows, including versioning, testing, and automated deployment.
• Develop scalable data pipelines for batch and real-time processing to support training and inference.
• Contribute to multi-agent/agent-based solutions for production workflows and troubleshoot distributed systems.

What you'll need

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
• Strong understanding of machine learning algorithms and statistical modeling.
• Experience with ML lifecycle management and tools such as AWS SageMaker, Azure ML, or Dataiku.
• Hands-on experience building RESTful APIs and deploying ML microservices using FastAPI or Flask.
• Working knowledge of Docker and Kubernetes for containerization and orchestration.
• Proficiency in Python and SQL.
• Exposure to data processing frameworks such as Spark, Hadoop, or NoSQL databases.

Details

• Location: Bangalore.