Note: The job is a remote job and is open to candidates in USA. Health Catalyst is one of the nation’s leading healthcare performance improvement companies, focused on data-informed healthcare improvement. They are seeking a Site Reliability Engineer on the Central AI team to help integrate AI into engineering workflows, evaluate architectures, and support AI governance frameworks.
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
- Software engineering background (any language) that allows you to read and reason about code, participate in architecture discussions, and credibly engage with engineering teams. Hands-on coding is not a primary responsibility of this role
- Experience with healthcare IT, including familiarity with clinical data models and interoperability standards such as HL7v2, CDA, EMR, and FHIR
- Knowledge of healthcare compliance and how it applies to AI systems and application security
- Experience with AI evaluation, testing, or red-teaming practices
- Familiarity with rules engines or deterministic workflow systems and how they compare to AI-native approaches in terms of reliability and auditability
- Experience with observability tooling such as Datadog, Grafana, or OpenTelemetry
- Agile/Scrum experience working within or alongside software engineering teams
- Experience building solutions in Databricks
Company Overview