Site Reliability Engineer- AI Enablement

Job description

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, AI Team: 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., 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. You may also bring this: 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 Preferred Experience & Education ( Your background might include) 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. Information Security and Compliance Responsibilities: 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. The above statements describe the general nature and level of work being performed in this job function. They are not intended to be an exhaustive list of all duties, and indeed additional responsibilities may be assigned by Health Catalyst The above statements describe the general nature and level of work being performed in this job function. They are not intended to be an exhaustive list of all duties, and indeed additional responsibilities may be assigned by Health Catalyst . Studies show that candidates from underrepresented groups are less likely to apply for roles if they don’t have 100% of the qualifications shown in the job posting. While each of our roles have core requirements, please thoughtfully consider your skills and experience and decide if you are interested in the position. If you feel you may be a good fit for the role, even if you don’t meet all of the qualifications, we hope you will apply. If you feel you are lacking the core requirements for this position, we encourage you to continue exploring our careers page for other roles for which you may be a better fit. At Health Catalyst, we appreciate the opportunity to benefit from the diverse backgrounds and experiences of others. Because of our deep commitment to respect every individual, Health Catalyst is an equal opportunity employer.