MLOps Engineer AWS SageMaker

We are seeking an experienced MLOps Engineer with strong expertise in AWS SageMaker to design, build, automate, and manage scalable machine learning platforms and production ML pipelines. The ideal candidate will have hands-on experience deploying machine learning models, implementing CI/CD for ML workflows, and managing cloud-native infrastructure on AWS.

Key Responsibilities

• Design, develop, and maintain end-to-end MLOps pipelines on AWS.
• Build and manage machine learning workflows using Amazon SageMaker.
• Deploy, monitor, and optimize ML models in production environments.
• Develop automated CI/CD pipelines for machine learning applications.
• Implement model versioning, model registry, feature stores, and monitoring solutions.
• Collaborate with Data Scientists, Data Engineers, and Software Engineers to operationalize ML models.
• Automate infrastructure provisioning using Infrastructure as Code (Terraform or CloudFormation).
• Manage containerized ML workloads using Docker and Kubernetes.
• Monitor model performance, data drift, and infrastructure health.
• Ensure security, scalability, reliability, and cost optimization across ML platforms.
• Document architecture, deployment processes, and operational best practices.

Required Qualifications

• 5+ years of experience in Machine Learning Engineering or MLOps.
• Strong hands-on experience with AWS cloud services.
• Deep expertise with Amazon SageMaker.
• Strong Python programming skills.
• Experience building CI/CD pipelines using Jenkins, GitHub Actions, GitLab CI, or Azure DevOps.
• Experience with Docker and Kubernetes.
• Experience with Infrastructure as Code using Terraform or CloudFormation.
• Knowledge of ML lifecycle management, model deployment, monitoring, and retraining.
• Experience with Git and software development best practices.

Preferred Qualifications

• Experience with MLflow, Kubeflow, Airflow, or Prefect.
• Experience with feature stores and model registries.
• Familiarity with Spark, Databricks, or distributed data processing.
• Experience with monitoring tools such as CloudWatch, Prometheus, or Grafana.
• AWS Certified Machine Learning Specialty or AWS Certified Solutions Architect certification is a plus.

Technical Skills

• AWS (EC2, S3, IAM, Lambda, ECR, ECS/EKS, CloudWatch)
• Amazon SageMaker
• Python
• Docker
• Kubernetes
• Terraform / CloudFormation
• CI/CD
• Git
• MLflow / Kubeflow
• Airflow / Prefect
• REST APIs

Nice to Have

• Experience with Generative AI or LLM deployment.
• Experience working in enterprise-scale production ML environments.
• Knowledge of security, governance, and compliance for AI/ML workloads.