Key Responsibilities:1. Design, implement, and maintain end-to-end ML pipelines for model training, evaluation, and deployment2. Collaborate with data scientists and software engineers to operationalize ML models3. Develop and maintain CI/CD pipelines for ML workflows4. Implement monitoring and logging solutions for ML models5. Optimize ML infrastructure for performance, scalability, and cost-efficiency6. Ensure compliance with data privacy and security regulationsRequired Skills and Qualifications:1. Strong programming skills in Python, with experience in ML frameworks2. Expertise in containerization technologies (Docker) and orchestration platforms (Kubernetes)3. Proficiency in cloud platform (AWS) and their ML-specific services4. Experience with MLOps tools5. Strong understanding of DevOps practices and tools (GitLab, Artifactory, Gitflow etc.)6. Knowledge of data versioning and model versioning techniques7. Experience with monitoring and observability tools (Prometheus, Grafana, ELK stack)8. Knowledge of distributed training techniques9. Experience with ML model serving frameworks (TensorFlow Serving, TorchServe)10. Understanding of ML-specific testing and validation techniques
Key Responsibilities: 1. Design, implement, and maintain end-to-end ML pipelines for model training, evaluation, and deployment 2. Collaborate with data scientists and software engineers to operationalize ML models 3. Develop and maintain CI/CD pipelines for ML workflows 4. Implement monitoring and logging solutions for ML models 5. Optimize ML infrastructure for performance, scalability, and cost-efficiency 6. Ensure compliance with data privacy and security regulations Required Skills and Qualifications: 1. Strong programming skills in Python, with experience in ML frameworks 2. Expertise in containerization technologies (Docker) and orchestration platforms (Kubernetes) 3. Proficiency in cloud platform (AWS) and their ML-specific services 4. Experience with MLOps tools 5. Strong understanding of DevOps practices and tools (GitLab, Artifactory, Gitflow etc.) 6. Knowledge of data versioning and model versioning techniques 7. Experience with monitoring and observability tools (Prometheus, Grafana, ELK stack) 8. Knowledge of distributed training techniques 9. Experience with ML model serving frameworks (TensorFlow Serving, TorchServe) 10. Understanding of ML-specific testing and validation techniques