Senior MLOps Engineer (AWS SageMaker & Airflow)

  Job Title: Senior MLOps Engineer (AWS SageMaker & Airflow)  Experience: 6–8 Years Location: Remote (India) Employment Type: Full-time  About the Role  We are looking for an experienced MLOps Engineer to join our cloud and AI engineering team.

This role is ideal for professionals with strong hands-on experience in AWS SageMaker–centric ML workflows and Apache Airflow–based orchestration , who can operationalize machine learning models at scale and ensure reliable, automated ML pipelines.  Key Responsibilities  ● 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  Required Skills & Qualifications  ● 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  Preferred / Nice-to-Have Skills  ● Experience with SageMaker Pipelines , Feature Store, or Model Registry  ● Knowledge of MLflow or experiment tracking tools  ● Exposure to Docker and Kubernetes  ● Understanding of data drift and concept drift detection  ● Experience with Terraform or Infrastructure as Code  Why Join Us  ● Work on large-scale, real-world ML systems  ● Fully remote role from India  ● Collaborate with global teams on cutting-edge AI initiatives  ● Opportunity to influence and mature MLOps practices at scale  Powered by JazzHR