Role Overview
We are seeking a SageMaker MLOps Developer to design and implement machine learning operations workflows on AWS SageMaker . This role focuses on automating ML pipelines, managing model lifecycle, and ensuring scalable, secure deployments in production environments.
Key Responsibilities
- Develop MLOps Pipelines
- Build and automate ML workflows using AWS SageMaker , SageMaker Pipelines , and Step Functions for training, deployment, and monitoring.
- Model Lifecycle Management
- Manage models through development, staging, and production using SageMaker Model Registry .
- CI / CD Integration
- Implement CI / CD pipelines for ML projects using AWS CodePipeline , CodeBuild , and Git-based workflows.
- Data Preparation & Feature Engineering
- Collaborate with data engineers and scientists to prepare datasets and optimize feature pipelines on AWS.
- Monitoring & Governance
- Set up monitoring for model performance, data drift, and compliance using Amazon CloudWatch , SageMaker Model Monitor , and AWS Config .
- Security & Compliance
- Ensure proper IAM roles, encryption, and governance for ML workflows.
Required Skills & Qualifications
Hands-on experience with AWS SageMaker , SageMaker Pipelines , and Model Registry .Strong programming skills in Python and SQL .Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn).Experience with AWS services (S3, Lambda, CloudWatch, IAM).Knowledge of CI / CD tools (AWS CodePipeline, Jenkins, GitHub Actions).Understanding of containerization (Docker) and orchestration (ECS / EKS).Preferred Qualifications
AWS Certified Machine Learning – Specialty or Solutions Architect.Experience with feature store concepts and ML observability tools.Exposure to Kubeflow or other ML orchestration frameworks.Soft Skills
Strong problem-solving and analytical skills.Excellent communication and teamwork abilities.