Site Reliability Engineer- AI Enablement

Health Catalyst

2d•Remote

About The Position

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.

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.
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.
Maintain compliance with training directives required by the organization pertaining to Information Security, Acceptable Use Policy and HIPAA Privacy and Security.
Adhere to and comply with the organizations Acceptable Use Policy.
Safeguard information system assets by identifying and reporting potential and actual security events to the organizations Security and Compliance Officers.

Nice To Haves

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.

Responsibilities

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.