MLOps & Deployment (Healthcare)

As an ML Engineer, you will be responsible for building and maintaining the pipelines that power AI in our Healthcare Information Systems (HIS). We are looking for a practical, detail-oriented engineer who is passionate about MLOps, data reliability, and production stability.

In this role, you won't just be building models; you will be ensuring those models work reliably in the real world. You will help bridge the gap between data science and software engineering by implementing automated workflows, managing cloud infrastructure, and ensuring our AI services are secure and scalable.

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.

Feature Management: Help build and maintain feature stores and data layers that ensure consistency between training and production environments.

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

Skills:

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.

3 5 years of professional experience in software engineering or data engineering, with at least 2 years focused on machine learning production environments.

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).

Data Tools: Experience with data processing frameworks (like Pandas, Spark, or dbt).

Education:

Additional qualifications that could help you succeed even further in this role include:

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 micro services architecture.