Associate Machine Learning Engineer

Welcome! We’re excited you’re considering an opportunity with us! To apply to this position and be considered, click the Apply button located above this message and complete the application in full. Below, you’ll find other important information about this position. The Associate Machine Learning Engineer supports the development, testing, and deployment of machine learning models that enhance healthcare operations and patient outcomes at WVU Medicine. Working under the guidance of senior engineers and data scientists, this role applies foundational machine learning and software engineering skills to build, evaluate, and deploy ML solutions. This position contributes to CI/CD pipelines, assists with model deployment and monitoring, and supports the integration of Generative AI and large language model (LLM) capabilities into enterprise systems. Follows established engineering and MLOps practices while developing skills in building production-grade systems. The Associate Machine Learning Engineer works with a variety of healthcare data sources, including electronic medical records, claims data, and unstructured data, to support the delivery of reliable, production-ready solutions. This role is suited for early-career professionals with a strong foundation in machine learning concepts, experience programming in Python, and an interest in applying AI within healthcare. MINIMUM QUALIFICATIONS : EDUCATION, CERTIFICATION, AND/OR LICENSURE: 1. Bachelor’s degree in Machine Learning, Computer Science, Mathematics, Data Science, or related field AND one (1) year of professional experience in machine learning or software engineering (internships, academic research, and capstone projects qualify). OR 2. Master’s degree in a related field with no prior professional experience required. PREFERRED QUALIFICATIONS : EDUCATION, CERTIFICATION, AND/OR LICENSURE: 1. Certifications or coursework related to machine learning, data science, or software engineering. EXPERIENCE: 1. Hands-on experience with machine learning and LLM (large language model) frameworks. CORE DUTIES AND RESPONSIBILITIES : The statements described here are intended to describe the general nature of work being performed by people assigned to this position. They are not intended to be constructed as an all-inclusive list of all responsibilities and duties. Other duties may be assigned. 1. Support data collection, cleaning, preprocessing, and feature engineering for ML model development. 2. Assist in training, evaluating, and validating machine learning models using Python and standard ML frameworks. 3. Write and maintain Python scripts and modules that are clean, documented, and production-oriented. 4. Contribute to CI/CD pipeline development and maintenance for automated model training, testing, and deployment in on-premise Linux-based environments. 5. Build and manage Docker containers for packaging ML models and dependencies for on-premise deployment on Linux-based servers. 6. Support productionalization of ML models, ensuring models are scalable, reliable, and ready for deployment at scale. 7. Assist in the integration and testing of Generative AI and LLM-powered features into healthcare applications and workflows. 8. Document model development processes, data pipelines, and deployment steps clearly and thoroughly. 9. Participate in Agile sprint ceremonies including standups, sprint planning, and retrospectives. 10. Collaborate with data scientists, ML engineers, and software engineers across functional teams. 11. Monitor deployed models for performance drift using available observability tools and contribute to retraining and improvement cycles. 12. Stay current with advancements in ML, Generative AI, LLMs, and open-source tools and frameworks. PHYSICAL REQUIREMENTS : The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. 1. Primarily sedentary office or remote work environment. 2. Prolonged periods working at a computer workstation. WORKING ENVIRONMENT : The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. 1. Normal office environment or remote work setting. SKILLS AND ABILITIES : 1. Proficiency in Python including data processing, model training, and API integration. 2. Working knowledge of CI/CD standards for systems deployment and monitoring 3. General understanding of Docker and container orchestration. 4. Understanding of Generative AI, LLMs, and prompt engineering principles. 5. Good written and verbal communication skills, including ability to document technical processes clearly. 6. F