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