What are the responsibilities and job description for the Machine Learning Engineer position at Keck Medicine of USC?
Requirements :
3 or more years relevant Machine Learning Engineer Experience
Bachelor’s Degree computer science, artificial intelligence, informatics or closely related field
Masters preferred
Healthcare Expertise : Understanding of healthcare regulations and standards, and familiarity with Electronic Health Records (EHR) systems, including integrating machine learning models with these systems.
Job Description :
Production Deployment and Model Engineering : Proven experience in deploying and maintaining production-grade machine learning models, with real-time inference, scalability, and reliability.
Scalable ML Infrastructures : Proficiency in developing end-to-end scalable ML infrastructures using on-premise cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Azure.
Engineering Leadership : Ability to lead engineering efforts in creating and implementing methods and workflows for ML / GenAI model engineering, LLM advancements, and optimizing deployment frameworks while aligning with business strategic directions.
AI Pipeline Development : Experience in developing AI pipelines for various data processing needs, including data ingestion, preprocessing, and search and retrieval, ensuring solutions meet all technical and business requirements.
Collaboration : Demonstrated ability to collaborate with data scientists, data engineers, analytics teams, and DevOps teams to design and implement robust deployment pipelines for continuous improvement of machine learning models.
Continuous Integration / Continuous Deployment (CI / CD) Pipelines : Expertise in implementing and optimizing CI / CD pipelines for machine learning models, automating testing and deployment processes.
Monitoring and Logging : Competence in setting up monitoring and logging solutions to track model performance, system health, and anomalies, allowing for timely intervention and proactive maintenance.
Version Control : Experience implementing version control systems for machine learning models and associated code to track changes and facilitate collaboration.
Security and Compliance : Knowledge of ensuring machine learning systems meet security and compliance standards, including data protection and privacy regulations.
Documentation : Skill in maintaining clear and comprehensive documentation of ML Ops processes and configurations.
Preferred :
Proficiency in Containerization Technologies : Experience with Docker, Kubernetes, or similar tools.
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