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