Bachelor’s or master’s degree in Computer Science, Engineering, Data Science, or a related field

Minimum of 3 years of experience in machine learning engineering, with a proven track record of deploying scalable ML solutions on AWS.

Strong proficiency in Python, with hands-on experience in ML libraries e.g., TensorFlow, PyTorch, Scikit-learn). 

Technical Skills and Experience:

AWS Expertise:

Extensive experience with AWS services related to ML and data processing, such as AWS Sagemaker, Lambda, S3, Glue, and EC2. Proficiency in building and managing data processing pipelines utilizing AWS technologies.

CI/CD Pipeline Development:

Solid understanding of CI/CD principles and experience in building and maintaining CI/CD pipelines specifically for ML workflows on AWS.

Familiarity with tools like AWS Code Pipeline, Code Build, and Jenkins or GitLab for automation. 

ML Engineering Architecture Optimization:

Experience in optimizing ML architectures on AWS, including effective use of Lambda functions for scalable, event-driven ML processes. Knowledge of containerization technologies (Docker, Kubernetes) and serverless architectures.

Model Governance and Deployment:

Proven ability to set up model governance frameworks ensuring model quality, reproducibility, and auditability.

Experience in developing model deployment pipelines, ensuring models are robustly tested, version-controlled, and easily rolled out or rolled back.

ML Model Development and Testing:

Strong background in developing, evaluating, and iterating on ML models to meet project objectives. Proficiency in model testing techniques, including A/B testing and performance monitoring.

Responsibilities:

Design, develop, and maintain scalable data processing and ML pipelines on AWS, ensuring best practices in data handling and processing.

Implement and optimize CI/CD pipelines for automated testing, integration, and deployment of ML models.

Collaborate with data scientists and engineers to optimize ML engineering architecture on AWS, focusing on efficiency and scalability. Establish robust model governance practices, including model versioning, auditing, and compliance with industry standards.

Continuously research and apply the latest ML technologies and methodologies to enhance project outcomes.