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
