Description
Mid-level MLOPS Engineer( Amazon SageMaker)
- MLOPS (Machine Learning Operations): The MLOPS Engineer will design and build scalable machine learning infrastructure, ensuring smooth deployment, monitoring, and lifecycle management of ML models.
- Responsibilities include automating workflows, enabling continuous integration/continuous deployment (CI/CD) pipelines.
- Develop and Maintain ML Infrastructure: Build and maintain ML pipelines that support model training, testing, deployment, and monitoring.
- Model Deployment: Implement efficient processes for deploying ML models in production environments, such as cloud platforms or on-premises infrastructure.
- Set up CI/CD pipelines for continuous integration and delivery of ML models. Automation and Scaling: Automate model retraining, validation, and performance monitoring processes.
- Collaboration with Data Scientists: Work closely with data scientists to streamline the model development lifecycle and ensure models can easily be transitioned to production.
- Monitoring and Optimization: Monitor ML models in production for accuracy and performance and troubleshoot any deployment or scaling issues.
- Infrastructure as Code (IaC): Develop infrastructure as code to manage cloud resources for ML workloads.
- Versioning and Experimentation Tracking: Implement model versioning, experiment tracking, and reproducibility techniques.
- Security and Compliance: Ensure models comply with organizational security standards and regulatory guidelines.
- Containerization: Hands-on experience with Docker, Kubernetes, or other container orchestration systems.
- CI/CD Tools: Knowledge of Jenkins, GitLab, CircleCI, or other CI/CD tools for automation.
- Data Pipelines: Experience orchestration tools for managing data pipelines.
- Version Control: Familiarity with Git for code versioning.
- DevOps Experience: Basic understanding of DevOps tools and practices (e.g., Terraform).
Required Skill
- Programming Languages: Proficiency in Python
- ML Frameworks: Experience with machine learning libraries such
- Cloud Platforms: Expertise in cloud platforms like Amazon SageMaker, Bedrock especially related to their AI/ML services.