Description

Build and deploy end-to-end machine learning systems on AWS SageMaker for scalable production environments.

This role is on-site.

Responsibilities

• Design and implement ML pipelines using SageMaker Pipelines, Feature Store, and Model Registry.
• Develop, tune, and monitor ML models in regulated environments, ensuring drift detection and versioning.
• Build scalable data engineering and feature engineering workflows using Python and large-scale datasets.
• Manage AWS infrastructure including S3, IAM, EC2, ECR, EKS, Lambda, and CloudWatch.
• Lead design discussions and collaborate with stakeholders to define technical requirements.

Required Skills

• 10+ years of experience in data science or machine learning engineering.
• Strong hands-on experience with AWS SageMaker for training, tuning, and deployment.
• Advanced proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Experience building scalable ML pipelines and MLOps practices (CI/CD, experiment tracking).
• Proficiency with Pyspark, SQL, and large-scale data processing.
• Production-grade software engineering skills: Git, APIs, Docker, and automation.
• Bachelor's degree in a technical field.

Preferred Skills

• Experience with Gen AI technologies and applications.
• Background in secure, regulated industry environments.

Key Skills

Education

Bachelor's degree