Machine Learning Engineer

Solventum develops healthcare solutions at the intersection of health, material, and data science to improve patient and professional outcomes. The Machine Learning Engineer will build and maintain MLOps pipelines, deploy and monitor machine learning models, integrate healthcare data, and ensure AI services are secure, scalable, and production-ready.

Responsibilities:

• Build and maintain CI/CD pipelines for machine learning, focusing on automated testing, model deployment, and version control (using tools like MLflow or Git)
• Deploy ML models as scalable APIs and microservices, ensuring they meet performance and latency requirements for clinical use
• Implement basic monitoring tools to track model performance, data drift, and system health in production
• Develop and optimize ETL processes to transform healthcare data (FHIR, HL7) into clean, usable datasets for model training and inference
• Help build and maintain feature stores and data layers that ensure consistency between training and production environments
• Work closely with backend teams to integrate ML outputs into our core healthcare applications
• Write clean, maintainable, and well-documented Python code. Participate in code reviews to ensure system reliability
• Use Docker and Kubernetes to package and orchestrate ML workloads across different environments
• Follow established protocols to ensure all data handling and deployments meet HIPAA and HITRUST security standards
• Responsibilities of this position include that corporate policies, procedures and security standards are complied with while performing assigned duties

Requirements:

• Bachelor's degree or higher in Computer Science, Software Engineering, Data Engineering, or a related field
• 6+ years of professional experience in software engineering or data engineering
• 5 years of experience in Python and familiarity with SQL
• 2 years of experience working in machine learning production environments
• 5 years of experience with AWS and containerization (Docker)
• 3 years of experience working with Java
• 3 years of experience with ML libraries (PyTorch or Scikit-learn) and MLOps tools (like Airflow, Prefect, BentoML, or Kubeflow)
• 4 years with data processing frameworks (like Pandas, Spark, or dbt)
• Familiarity with deploying Large Language Models (LLMs) or using frameworks like LangChain
• Experience working in a regulated environment (Healthcare, Finance, etc.)
• Understanding of API design and microservices architecture