Sutter Health

AI Engineer

Sutter Health San Francisco, CA, US
Full-time $172k - $276k Posted 3 days ago

Role overview

Responsible for leading the design, build, and maintain Sutter Health’s artificial intelligence (AI) and machine learning (ML) infrastructure, including end to end pipelines for ML and large language models (LLM) that support analytics, data science, and enterprise AI use cases. This includes how AI models and associated data are ingested, processed, trained, deployed, monitored, governed, and secured across cloud and on premises environments. Ensures that AI systems meet high standards of performance, reliability, and compliance, enabling the organization to safely and effectively integrate AI capabilities into clinical, operational, and strategic workflows.

Utilizing modern artificial intelligence operations (AIOps) and machine learning operations (MLOps) practices, leads the operationalization of models, maintenance of scalable AI services, monitoring of system behavior, and automation of deployment workflows. Works with structured and unstructured data, clinical data models, healthcare data standards, and modern cloud platforms. Develops and validates ML models, integrates researcher built or vendor provided algorithms, and contributes to AI platform architecture and tooling. Sets standards for high quality, secure, and well governed AI systems that support advanced analytics, automation, and intelligent applications.

Basic qualifications

  • Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or related field; or equivalent combination of education and experience.

Preferred qualifications

  • Advanced experience with Azure cloud services, including containerized model hosting, Azure ML, secure environment management, and cloud‑native deployment patterns.
  • Strong proficiency in MLOps / LLMOps practices, including CI/CD pipelines, automated testing, observability, model versioning, canary/AB rollouts, and drift monitoring.
  • Hands‑on expertise in building and operating AI/ML/LLM pipelines (ingestion preprocessing training inference monitoring), using Python, modern container frameworks, and orchestration systems.
  • Experience integrating researcher‑built or vendor‑provided ML/LLM models into production workflows, including API‑based embedding, performance tuning, and secure data handling.
  • Familiarity with healthcare data standards and environments, such as clinical data models, unstructured clinical text, and privacy/security expectations in regulated domains.

Benefits

  • check_circle Yes

Tags & Focus Areas

Fulltime Ai Ai Engineer Machine Learning Data Science Generative Ai

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