MLOPS Engineer

Key Responsibilities:

• Design, implement, and maintain end-to-end ML pipelines for model training, evaluation, and deployment
• Collaborate with data scientists and software engineers to operationalize ML models
• Develop and maintain CI/CD pipelines for ML workflows
• Implement monitoring and logging solutions for ML models
• Optimize ML infrastructure for performance, scalability, and cost-efficiency
• Ensure compliance with data privacy and security regulations

Required Skills and Qualifications:

• Strong programming skills in Python, with experience in ML frameworks
• Expertise in containerization technologies (Docker) and orchestration platforms (Kubernetes)
• Proficiency in cloud platform (AWS) and their ML-specific services
• Experience with MLOps tools
• Strong understanding of DevOps practices and tools (GitLab, Artifactory, Gitflow etc.)
• Knowledge of data versioning and model versioning techniques
• Experience with monitoring and observability tools (Prometheus, Grafana, ELK stack)
• Knowledge of distributed training techniques
• Experience with ML model serving frameworks (TensorFlow Serving, TorchServe)
• Understanding of ML-specific testing and validation techniques