MLOps Engineer

Machine Learning Platform Engineer - Healthcare Environment

Contract  MN - 100 % Remote

Need - Healthcare Environment 

Note:  This role has the potential to be converted into a FULL‑TIME position.

Please submit candidates who are eligible for conversion to FTE.

Skills:

1. Machine Learning
2. CI/CD
3. Python
4. MLOps
5. MLflow
6. Git
7. Docker
8. Spark
9. REST APIs
10. Kubernetes
11. Airflow/Prefect/Kubeflow
12. ETL & Data Pipelines
13. AWS/Azure healthcare environment
14. FHIR/HL7/HIPAA
15. LLM/LangChain

Key Responsibilities

MLOps & Deployment

Pipeline Development: Build and maintain CI/CD pipelines for machine learning, focusing on automated testing, model deployment, and version control (using tools like MLflow or Git).

Model Serving: Deploy ML models as scalable APIs and microservices, ensuring they meet performance and latency requirements for clinical use.

Monitoring: Implement basic monitoring tools to track model performance, data drift, and system health in production.

Data Engineering & Integration

Data Pipelines: Develop and optimize ETL processes to transform healthcare data (FHIR, HL7) into clean, usable datasets for model training and inference.

System Integration: Work closely with backend teams to integrate ML outputs into our core healthcare applications.

Engineering Best Practices

Code Quality: Write clean, maintainable, and well-documented Python code. Participate in code reviews to ensure system reliability.

Containerization: Use Docker and Kubernetes to package and orchestrate ML workloads across different environments.

Security & Compliance: Follow established protocols to ensure all data handling and deployments meet HIPAA and HITRUST security standards

Your Skills and Expertise

To set you up for success in this role from day one, Solventum requires (at a minimum) the following qualifications:

Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, or a related field.

AND

Programming: Strong proficiency in Python and familiarity with SQL. Knowledge of a compiled language (like Go or Java) is a plus.

Cloud & Infrastructure: Hands-on experience with at least one major cloud provider (AWS, Azure, or Google Cloud Platform) and containerization (Docker).

ML Tools: Familiarity with ML libraries (PyTorch or Scikit-learn) and MLOps tools (like Airflow, Prefect, BentoML, or Kubeflow).