MLOps Engineer — AWS SageMaker

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.