Job Description :
• Design and implement cloud solutions, build MLOps on cloud (AWS, Azure, or GCP)
• Build CI/CD pipelines orchestration by GitLab CI, GitHub Actions, Circle CI, Airflow or similar tools
• Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality
• Data science models testing, validation and tests automation
• Communicate with a team of data scientists, data engineers and architect, document the processes
• Infrastructure Setup:** Designing and deploying AWS infrastructure components such as EC2 instances, S3 buckets, VPCs, and networking configurations tailored for machine learning workloads.
• Automated Model Deployment:** Developing automated pipelines for model training, evaluation, and deployment using AWS services like SageMaker, AWS Lambda, and AWS Batch.
• Recommendation and Content Optimisation using AWS Personalization.Create and Manage batch inference job to get batch item recommendations for users based on input data from Amazon S3.
• Monitoring and Optimization:** Implementing monitoring solutions to track model performance, resource utilization, and overall system health. Employing AWS CloudWatch, AWS X-Ray, or custom monitoring tools.
• Security and Compliance:** Ensuring the security of data and models by implementing encryption, access controls, and compliance measures following AWS best practices and industry standards
