AI Engineer

**Please Note: While this position is listed as hybrid, regular in-office attendance is required. Candidates should be prepared to commute to the San Francisco office on a consistent basis to support team collaboration and business needs.**

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., **Please Note: While this position is listed as hybrid, regular in-office attendance is required. Candidates should be prepared to commute to the San Francisco office on a consistent basis to support team collaboration and business needs.**

• Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or related field; or equivalent combination of education and experience., * 5 years recent relevant experience, * 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.

SKILLS AND KNOWLEDGE:

• Relevant experience in AI/ML Engineering and LLM/MLOps
• Experience building end to end AI/ML pipelines, including training, deployment, monitoring, and retraining loops.
• Strong programming skills in Python and familiarity with modern ML/LLM frameworks and libraries.
• Hands on experience with Azure, including services such as Fabric, Foundry, Container Apps, AKS, and Application Insights.
• Experience implementing AIOps / MLOps / LLM Ops practices, including CI/CD pipelines, automated testing, observability, and versioned deployments.
• Experience with GitOps principles using GitHub Actions or similar tools.
• Understanding of healthcare data models, including Epic Clarity, FHIR, HL7, and Caboodle.
• Ability to evaluate and integrate machine learning models into production systems and support model lifecycle management.
• Strong debugging skills across distributed systems, containerized environments, and cloud platforms.
• Ability to work in cross functional environments with clinicians, data scientists, architects, and engineering teams.
• Ability to produce high quality documentation and communicate complex technical concepts to diverse stakeholders.
• Detail oriented, organized, and effective at prioritizing multiple concurrent initiatives.
• Familiarity with HIPAA, PHI/PII security requirements, and regulatory considerations for healthcare AI.

These Principal Accountabilities, Requirements and Qualifications are not exhaustive but are merely the most descriptive of the current job. Management reserves the right to revise the job description or require that other tasks be performed when the circumstances of the job change (for example, emergencies, staff changes, workload, or technical development).