About the RoleWe are looking for an experienced MLOps Engineer to design, build, and scale machine learning infrastructure and deployment pipelines on AWS. You will collaborate closely with data scientists, ML engineers, and DevOps teams to ensure efficient, reliable, and secure delivery of ML solutions in production environments.Key Responsibilities Design, build, and maintain end-to-end MLOps pipelines supporting model training, testing, deployment, and monitoring. Implement and manage CI/CD pipelines for machine learning workflows (GitHub Actions, GitLab, Jenkins, or CodePipeline). Build scalable containerized solutions using Docker and Kubernetes (ECS/EKS). Automate and orchestrate ML pipelines using SageMaker Pipelines or Kubeflow. Manage model tracking and registry through MLflow or SageMaker Model Registry. Implement model monitoring and drift detection using SageMaker Model Monitor or custom tools. Develop data engineering workflows leveraging Glue, EMR, Spark, and PySpark. Apply Infrastructure as Code (IaC) principles using Terraform or AWS CloudFormation. Ensure system reliability and observability with CloudWatch, Prometheus, ELK, or Datadog. Follow AWS security best practices (IAM, VPC, KMS, Secrets Manager, PrivateLink). Collaborate with cross-functional teams to integrate ML solutions seamlessly into production systems. Requirements 5+ years of hands-on experience in MLOps or ML Engineering. Strong expertise with AWS services, including SageMaker, Lambda, ECR, ECS/EKS, S3, and Step Functions. Proficient in Python and ML frameworks such as PyTorch, TensorFlow, or Scikit-learn. Solid understanding of CI/CD automation, containerization, and infrastructure automation. Strong analytical mindset, problem-solving skills, and ability to work in agile, cross-functional teams. Nice to Have Experience with multi-cloud or hybrid ML environments. Familiarity with cost optimization and governance for ML workloads. AWS or MLOps-related certifications (e.g., AWS Certified Machine Learning – Specialty).