ML Ops Engineer

Machine Learning Platform Engineer - Remote

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:

Your Skills and Expertise

To set you up for success in this role from day one, Client 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 microservices architecture.