MLOps _ ML Engineer

Key Responsibilities:

• Architect and implement scalable MLOps pipelines for model development, deployment, and monitoring
• Lead end-to-end operationalization of ML models using AWS SageMaker and AWS ecosystem
• Build and manage CI/CD pipelines for ML workflows using tools like GitHub Actions, Jenkins, or AWS CodePipeline
• Automate key model lifecycle stages including training, version control, deployment, and rollback
• Collaborate closely with data scientists, ML engineers, and DevOps for seamless model integration
• Monitor live models for performance degradation, data drift, and reliability issues
• Establish governance and best practices for reproducibility, security, and compliance in ML systems

Required Skills:

• 10+ years of experience in MLOps, ML engineering, or similar domains
• Proven hands-on expertise with AWS SageMaker, Lambda, S3, CloudWatch, and other AWS services
• Strong Python programming and experience with Docker, Kubernetes, and Terraform
• Deep understanding of infrastructure-as-code and CI/CD tools
• Familiarity with model monitoring frameworks such as Prometheus, Grafana, or Evidently
• Solid grasp of ML algorithms, feature engineering, and production model deployment

Preferred Qualifications:

• AWS Certified Machine Learning – Specialty or AWS DevOps Engineer certification
• Knowledge of feature stores, model registries, and real-time inference platforms
• Experience leading cross-functional AI/ML engineering teams