Health Catalyst

Site Reliability Engineer- AI Enablement

Rol remoto de Site Reliability Engineer con fit claro de ubicación del candidato.

Publicado13 jul 2026

Países elegibles1 país aceptado

Señal de senioritySenior

Modelo de trabajoRemoto

Ubicaciones aceptadas para candidatos

Estados Unidos

AWS Azure CI/CD Docker Kubernetes LLM

Resumen del rol

Site Reliability Engineer- AI Enablement

Requisitos y responsabilidades

Contenido del rol extraído en secciones para revisar más rápido.

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

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

You may also bring this:

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.

You may also bring this:

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.

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