Design, build, and maintain end-to-end MLOps pipelines using AWS SageMaker
Develop and manage Airflow DAGs for ML workflow orchestration (training, validation, deployment, retraining)
Automate model training, evaluation, versioning, and deployment
Implement CI/CD pipelines for ML workflows and model releases
Manage model lifecycle, including experimentation, deployment, monitoring, and retraining
Integrate data ingestion and feature engineering workflows with ML pipelines
Monitor model performance, data drift, and pipeline reliability
Collaborate closely with Data Scientists, Data Engineers, and DevOps teams
Ensure security, scalability, and cost optimization across ML infrastructure

Requirements

6–8 years of experience in MLOps, ML Engineering, or DevOps for ML
Strong hands-on experience with AWS SageMaker (training jobs, endpoints, pipelines, model registry)
Solid experience with Apache Airflow for workflow orchestration
Proficiency in Python for ML and pipeline development
Experience building and maintaining production-grade ML pipelines
Hands-on experience with AWS services such as S3, IAM, EC2, ECR, CloudWatch
Familiarity with CI/CD tools (GitHub Actions, Jenkins, GitLab CI, etc.)
Strong understanding of Linux environments and cloud networking basics
Experience with monitoring, logging, and alerting for ML systems

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

MLOpsML EngineeringDevOpsAWS SageMakerApache AirflowPythonCI/CDLinuxCloud NetworkingMonitoring

Soft Skills

CollaborationCommunicationProblem SolvingOrganizational Skills