AI Engineer
Sutter Health5 days ago
Base Salary
$173k - $277k/yr
Responsibilities
- Design, build, and maintain AI/ML and LLM infrastructure across cloud and on-premises environments.
- Develop and operate end-to-end pipelines spanning data ingestion, preprocessing, training, inference, monitoring, and retraining.
- Operationalize models and maintain scalable AI services using AIOps, MLOps, and LLMOps practices.
- Automate deployment workflows and implement testing, observability, model versioning, canary or A/B rollouts, and drift monitoring.
- Integrate researcher-built and vendor-provided models into secure production workflows, including API-based embedding and performance tuning.
- Contribute to AI platform architecture, tooling, governance, security, and healthcare data integration.
- Debug distributed systems, containerized environments, and cloud platforms while supporting model lifecycle management.
- Collaborate with clinicians, data scientists, architects, and engineering teams and document complex technical concepts.
Requirements
- Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field, or an equivalent combination of education and experience.
- At least 5 years of recent relevant experience.
- Advanced Azure experience, including containerized model hosting, Azure ML, secure environment management, and cloud-native deployment patterns.
- Strong proficiency in MLOps and LLMOps, including deployment pipelines, automated testing, observability, versioning, canary or A/B rollouts, and drift monitoring.
- Hands-on experience building and operating AI, ML, and LLM pipelines with Python, container frameworks, and orchestration systems.
- Experience integrating ML or LLM models into production workflows and handling data securely.
- Understanding of healthcare data models and standards, including Epic Clarity, FHIR, HL7, and Caboodle.
- Experience with GitOps practices using GitHub Actions or similar tools.
- Familiarity with HIPAA, PHI/PII security requirements, and regulatory considerations for healthcare AI.
- Strong debugging, documentation, communication, organization, prioritization, and cross-functional collaboration skills.
Benefits
- Full-time regular position with a Monday–Friday day schedule and 40-hour workweek.
- Hybrid position requiring consistent in-office attendance in the San Francisco office; weekend work may be needed.
- Eligible positions include a comprehensive benefits package.
Categories