MLOps Engineer

Location: Bangalore / Lucknow/ Pune

Work Model- Hybrid

Experience: 4-6+Years

Company’s website: https://usefulbi.com

LinkedIn link: UBI LinkedIn

Role Overview: We are looking for an experienced MLOps Engineer with strong hands-on expertise in AWS, Amazon SageMaker, MLflow, Python, and CI/CD to build, automate, and manage scalable machine learning operations and deployment pipelines.

The ideal candidate should have experience in designing end-to-end ML pipelines, model deployment, model monitoring, model lifecycle management, and automation using AWS services such as SageMaker, Step Functions, EventBridge, and Model Registry.

Key Responsibilities:

• Design, build, and maintain end-to-end MLOps pipelines for machine learning model development, training, validation, deployment, and monitoring.
• Develop and manage ML workflows using Amazon SageMaker , including training jobs, processing jobs, pipelines, endpoints, and model deployment.
• Implement model versioning and lifecycle management using SageMaker Model Registry and/or MLflow .
• Build workflow orchestration using AWS Step Functions for automated ML pipelines.
• Use Amazon EventBridge to implement event-driven automation and trigger ML workflows.
• Develop reusable and production-ready automation scripts using Python .
• Implement and maintain CI/CD pipelines for ML model and application deployment.
• Automate model training, validation, registration, approval, and deployment processes.
• Integrate MLflow for experiment tracking, model versioning, artifact management, and model lifecycle management.
• Deploy and manage ML models across AWS environments while following scalability, security, and reliability best practices.
• Implement monitoring and alerting for deployed models and ML infrastructure.
• Troubleshoot issues related to model deployment, pipelines, infrastructure, and production ML workloads.
• Collaborate with Data Scientists, Data Engineers, Software Engineers, and Cloud/DevOps teams to operationalize machine learning models.
• Follow best practices for Infrastructure as Code, version control, security, logging, monitoring, and automation .

Required Skills:

• Strong hands-on experience with AWS Cloud .
• Strong experience with Amazon SageMaker and its ML lifecycle capabilities.
• Strong knowledge of MLflow for experiment tracking and model lifecycle management.
• Hands-on experience with AWS Step Functions for workflow orchestration.
• Experience with Amazon EventBridge and event-driven architectures.
• Strong programming skills in Python .
• Experience designing and implementing CI/CD pipelines for ML workloads.
• Experience with Git and source-code management.
• Understanding of Docker/containerization and deployment of ML workloads.
• Good understanding of Machine Learning lifecycle and MLOps practices .