Site Reliability Engineer- AI Enablement

Join one of the nation’s leading and most impactful health care performance improvement companies. Over the years, Health Catalyst has achieved and documented clinical, operational, and financial improvements for many of the nation’s leading healthcare organizations. We are also increasingly serving international markets. Our mission is to be the catalyst for massive, measurable, data-informed healthcare improvement through:

Data: integrate data in a flexible, open & scalable platform to power healthcare’s digital transformation

Analytics: deliver analytic applications & services that generate insight on how to measurably improve

Expertise: provide clinical, financial & operational experts who enable & accelerate improvement

Engagement: attract, develop and retain world-class team members by being a best place to work

Role:
Site Reliability Engineer, AITeam:
Central AI

Location:

Remote Travel:
None
** This position is currently not eligible for visa sponsorship
** Be part of a World Class Team responsible for:

As a Site Reliability Engineer on the Central AI team, you will help Health Catalyst engineer teams adopt AI responsibly and effectively. You bring deep experience solutioning and implementing AI systems, and you use that expertise to evaluate architectures, guide engineering teams on AI tooling and workflows, and support the organization’s AI governance framework. You are a trusted partner and advisor — not a gatekeeper — helping teams build AI-powered systems that are reliable, observable, and aligned with our standards.

What you will own in this role…AI Workflow Enablement:
Train and coach engineering teams on how to effectively integrate AI into their development workflows, including the use of AI-assisted coding tools, prompt engineering practices, and agentic development patterns.

Architecture Review:
Evaluate AI system designs submitted through the Central AI intake process, providing actionable guidance on integration patterns, reliability risks, observability gaps, and alignment with AI governance standards.

AI Governance Guidance:
Serve as a technical resource for the organization’s AI governance framework — helping teams understand and apply policies around model access, data handling, risk tiers, and responsible AI use in practice.

Solutioning & Implementation Support:
Partner with engineering teams during the design and implementation phases of AI projects, offering hands-on guidance on LLM integration, RAG pipelines, agentic architectures, and AI service patterns.

Reliability Advising:
Bring an SRE perspective to AI systems — advising teams on observability, SLOs, failure modes, and operational readiness for AI-powered services. Participate in incident calls as a subject matter expert to provide AI-specific guidance when needed.

Tooling & Standards:
Contribute to the development of internal standards, reference architectures, and reusable patterns that make it easier for teams to build AI systems correctly the first time.

Cross-functional Collaboration:

Work closely with product managers, data scientists, security, and compliance stakeholders to ensure AI implementations meet organizational, regulatory, and clinical requirements.

Documentation:
Maintain clear documentation of AI architecture patterns, governance guidance, and review decisions to support knowledge sharing and organizational learning.

Continuous Learning:
Stay current with the rapidly evolving AI landscape — LLM capabilities, agentic frameworks, AI safety research, and SRE practices for AI systems — and bring relevant insights back to the team.

What you bring to the role:

Proven experience solutioning and implementing AI systems in production, including LLM API integration (e.g., Azure AI Foundry, Anthropic Claude) and AI-native application patterns.

Hands-on experience with at least one agentic or RAG framework (e.g., Lang Chain, Llama Index, Semantic Kernel, or similar).Strong SRE or platform engineering background, with working knowledge of observability, reliability principles, and operational best practices.

Ability to evaluate AI architectures for reliability, security, governance alignment, and operational readiness —…