Senior MLOps Engineer (AWS SageMaker & Airflow)

Remote

Full Time

Mid Level

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

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