Software Engineer (MLOps) – Early Disease Detection Clinical AI

Responsibilities

• Collaborate with clinical experts, data scientists, and software developers to translate business and clinical needs into robust, scalable MLOps solutions.
• Design, build, and optimize end-to-end automated pipelines for clinical model training, retrospective validation, deployment, and monitoring.
• Work with data scientists to implement automated model evaluation, benchmarking, and retraining logic, ensuring deep alignment between model performance metrics and production guardrails.
• Collaborate with the Data Engineering team to define and consume standardized datasets from common clinical data models (e.g., OMOP), ensuring models are fed with highly structured, clean clinical data.
• Maintain and expand our automated model deployment pipeline, including all model, code, data artifacts, workflows, and repositories, to enable seamless, “push-button” production deployments and automated retrospective validations.
• Implement automated monitoring, logging, and alerting systems to track model inputs, output feature drift, and operational latency in Google Cloud Platform (GCP) production environments.
• Manage and optimize cloud infrastructure for machine learning workloads specifically on Google Cloud Platform (GCP).
• Create and maintain clear architecture diagrams to document, justify, and communicate decisions for new MLOps infrastructure and patterns.
• Write and maintain highly performant, production-grade applications and automation scripts for core MLOps services.
• Contribute to robust clinical model governance practices, including drafting and automating model documentation templates (such as CHAI Model Cards) to track clinical bias, model performance parameters, and data drift over time.
• Stay updated with the latest trends in MLOps, deployment patterns, and healthcare-focused model governance.

Qualifications

• Bachelor’s degree or higher in computer science, data science, statistics, biomedical engineering, or a related field (or equivalent practical experience).
• 3+ years of professional experience in software engineering, with a focus on machine learning operations (MLOps) or production platform engineering.
• Proficiency in Python, with exposure to common data science libraries and frameworks (such as NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch).
• Experience deploying, hosting, and managing the deployment pipeline for AI/ML models on Google Cloud Platform (GCP).
• Familiarity with clinical data standards (such as OMOP, FHIR, DICOM) and familiarity with clinical terminologies (such as ICD, SNOMED, LOINC).
• Experience working in a PHI-regulated environment or HIPAA-compliant secure cloud environment.
• Strong analytical, problem-solving, and communication skills, with a passion for improving healthcare outcomes.
• Familiarity or professional experience with Golang for systems development and backend services is preferred.
• Practical experience or a background in machine learning model development is preferred, particularly with a strong understanding of how to evaluate model performance, detect data leakage, and diagnose training-versus-production discrepancies.