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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.
Job Responsibility:
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
Nice to have:
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
What we offer:
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