MLOPS
About UsefulBI:
UsefulBI is a leadingAI-driven data solutions provider specializing in data engineering, cloud transformations, and AI powered analytics for Fortune 500 companies.
We help businesses turn complex data into actionable insights through our innovative products and services.
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.