AI/ML Engineer

Children's Healthcare of Atlanta • Brookhaven, GA

8d

About The Position

The AI Machine Learning Engineer bridges data science and implementation, supporting data scientists and clinical informaticists in developing, deploying, and maintaining production-grade applications that use machine learning and artificial intelligence. The role partners with clinicians, subject matter experts, analytics developers, researchers, and data engineers to deliver actionable, real-time insights aligned with clinical, financial, and operational goals. The engineer also works with analytics leaders and business partners to apply advances in ML and AI to Children’s analytics.

Requirements

• At least 2 years of experience in a machine learning engineering, data science, or data engineering role.
• At least 1 year of experience implementing machine learning algorithms in a production environment or applying software development lifecycle principles to analytics.
• Bachelor’s degree in Information Systems, Data Science, Machine Learning, Computer Science, Industrial Engineering, or Analytics.
• A master’s degree in one of the listed fields, or four years of industry experience in lieu of a master’s degree.
• Familiarity with electronic health records such as Epic or Cerner, ERP systems such as Workday, or other business systems used at Children’s.
• Familiarity with cloud computing platforms such as Azure, AWS, or Google Cloud, particularly machine learning and data science tools such as Databricks, Snowflake, and Azure ML.
• Familiarity with using open-source foundation large language models to build custom domain-specific applications.
• Working knowledge of machine learning concepts, including feature engineering, hyperparameter tuning, bias and variance tradeoffs, model and data drift, and common model-performance metrics.
• Knowledge of data structures and data modeling principles, and ability to ingest and process large datasets using Python, SQL, PySpark, and Scala.
• Knowledge of software development methodologies and architecture, including API web services.
• Ability to write robust, production-grade code for predictive analytics applications using languages such as Python, R, Java, or Scala.
• Ability to apply coding standards and best practices to data and machine learning pipelines, including code packaging, unit testing, data validation, containerization, automation, and CI/CD.
• Ability to create effective technical documentation and communicate requirements to business partners and vendors.
• Ability to work and provide coaching in agile, iterative frameworks, develop in shared coding environments, and collaborate with developers using source control tools such as Git.

Nice To Haves

• Experience developing software and data infrastructure to support validation and operationalization of predictive models, including tools for analysis, model optimization, drift detection, model versioning, deployment, and performance and feature monitoring.
• Experience with model training and inference in common machine learning libraries and frameworks such as scikit-learn, H2O, TensorFlow, PyTorch, or DataRobot.
• Experience working with payer or provider healthcare data in a HIPAA-regulated environment.
• Epic certification or badges in Cogito, Cognitive Computing Platform, Chronicles, and Interconnect.
• Azure certifications in Data Fundamentals, AI Fundamentals, Data Science, or Data Engineering.

Responsibilities

• Work with data scientists, data engineers, and software engineers to develop and optimize reproducible, reusable pipelines for model training, validation, and inference.
• Monitor and troubleshoot AI and ML systems to maintain availability, performance, and reliability.
• Partner with subject matter experts and non-technical stakeholders to understand needs and capture clinical and business-system workflows when designing AI solutions.
• Improve the model development lifecycle by defining technical standards and reducing unnecessary complexity.
• Lead code reviews and debugging sessions, and mentor data scientists and analytics developers to improve code quality.
• Follow technical guidelines and best practices for project management, source control, documentation, and responsible and ethical AI.
• Collaborate with enterprise architects, analytics platform services, and technical leaders to create and maintain a current machine learning architecture roadmap.
• Partner with researchers on translational projects aligned with quality and operational goals, ensuring technical standards and regulatory compliance are followed.
• Contribute to organizational knowledge through internal knowledge shares and seminars, research conferences, and online learning.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level