Note: The job is a remote job and is open to candidates in USA. Health Catalyst is one of the nation’s leading health care performance improvement companies, focusing on data-informed healthcare improvement. The Site Reliability Engineer on the Central AI team will help integrate AI into development workflows, evaluate AI architectures, and support AI governance, ensuring reliable and effective AI systems.
Responsibilities
- 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
Skills
- 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
- BS/BA or MS in Computer Science, Information Systems, or a related technical field — or equivalent practical experience
- A minimum of 5 years of experience in site reliability engineering, platform engineering, or a closely related role
- At least 2 years of hands-on experience solutioning or implementing AI/LLM-based systems in a production or near-production context
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