Machine Learning Engineer

Position Overview:

• Collaborate with data engineers to develop and deploy machine learning models.
• Build and maintain scalable machine learning solutions in production.
• Create secure AWS SageMaker Endpoints and Lambdas to operationalize machine learning models.
• Develop and maintain secure, robust, and scalable data pipelines.
• Implement MLOps best practices regarding data and model drifts checks to ensure data quality and model accuracy and reliability via appropriate monitoring and troubleshooting.
• Integrate MLOps checks and stages (e.g., automated model deployment following successful re-training) within the CI/CD pipeline.
• Collaborate with other teams to define request and response formats for services and define the appropriate AWS service to use, depending on the use case.

Qualifications:

• Bachelors degree in computer science, STEM, or a related field. Equivalent professional experience will also be considered.
• At least 3 years of professional experience in Machine Learning Engineering, with a focus on deploying secure and robust models in production.
• Strong programming skills in Python, with experience in libraries such as scikit-learn, pandas, scipy, click, and flask and/or FastAPI.
• Experience with the AWS ecosystem, specifically with services like SageMaker and Lambda.
• Experience working with Healthcare data (Claims, EMR, MA data) and a good understanding of Diagnosis codes, Drug codes, Procedure codes, etc. (ICD-10, NDCs, J codes, etc.)
• Experience working in a cross-functional team in a professional setting.
• Good understanding of software development lifecycle and practices, including Git and version control, code reviews, and functional, unit and integration testing.
• Excellent problem-solving skills and the ability to work independently.

Bonus points if you have experience in:

• Experience managing the lifecycle of a machine learning model, including updates, tuning and retraining.
• Familiarity with AWS services beyond SageMaker and Lambda, such as S3, EC2, API Gateway, ECR, and DynamoDB.
• Experience working with large and complex data sets, including data cleaning and preprocessing.
• Knowledge of advanced machine learning techniques (neural networks, ensemble learning, reinforcement learning, etc.) and the ability to implement them in Python.
• Experience with Docker or other containerization technologies.
• Familiarity with CI/CD processes, especially as applied to ML operations (MLOps).