DevOps Track Architect

• 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.