Site Reliability Engineer – AI Enablement

Job not on LinkedIn

🕒 July 8

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

⛑ DevOps & Site Reliability Engineer (SRE)

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Health Catalyst

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2008

🏥 Healthcare

💼 Consulting

📦 Logistics

Healthcare • Consulting • Logistics

Health Catalyst is a leading provider of data and analytics technology and services to healthcare organizations, committed to being the catalyst for massive, measurable, data-informed healthcare improvement. The company empowers organizations with AI-enabled insights and comprehensive data solutions to drive scalable, measurable improvements in patient outcomes, operational efficiency, and financial performance. With a focus on population health management, clinical quality, and patient engagement, Health Catalyst aims to transform healthcare through data-driven decision-making.

📋 Description

• As a Site Reliability Engineer on the Central AI team, you will help Health Catalyst engineer teams adopt AI responsibly and effectively. • 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. • 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. • 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. • 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. • 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. • 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. • Work closely with product managers, data scientists, security, and compliance stakeholders to ensure AI implementations meet organizational, regulatory, and clinical requirements. • Maintain clear documentation of AI architecture patterns, governance guidance, and review decisions to support knowledge sharing and organizational 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.

🎯 Requirements

• 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., LangChain, LlamaIndex, 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 — and communicate findings clearly to both technical and non-technical audiences. • Experience advising or enabling engineering teams: coaching, conducting reviews, or leading training on AI tooling and best practices. • Familiarity with AI governance concepts, including risk tiering, responsible AI principles, prompt safety, and access control for AI services. • Cloud infrastructure experience with Azure or AWS, including managed AI/ML services. • Familiarity with container-based architectures (Docker, Kubernetes) and CI/CD pipelines. • Strong written and verbal communication skills; able to articulate complex AI concepts to audiences of varying technical background. • Highly collaborative, self-directed, and motivated by helping others succeed with new technology.

🏖️ Benefits

• Flexible PTO • Professional development stipend • Remote-first work environment