Site Reliability Engineer – AI Enablement

Vaga não está no LinkedIn

🕒 Julho 8

Candidatar-se

Encontrar Vagas Remotas Similares

Configurar Alerta de Email para Novas Vagas

📊 Verifique sua pontuação de currículo para esta vaga

Melhore suas chances de conseguir uma entrevista verificando sua pontuação de currículo antes de se candidatar.

Enviar Currículo

HealthCatalyst Norway

SiteLinkedInTodas as Vagas

2 - 10 funcionários

Fundada em 2011

🏥 Saúde

💼 Consultoria

🤝 B2B

Healthcare • Consulting • B2B

HealthCatalyst Norway é uma iniciativa (HealthCatalyst AS) que desenvolve a Noruega como um centro global de inovação e teste para a indústria da saúde, aumenta as exportações relacionadas à saúde e fortalece a colaboração público-privada. A empresa opera programas de exportação e aceleração que ajudam empresas norueguesas de tecnologia da saúde a entrarem no mercado alemão, oferecendo serviços de consultoria, orientação de mercado, networking, visibilidade em feiras comerciais (por exemplo, DMEA), acesso a investidores e eventos direcionados. A organização colabora com a Innovation Norway, Team Norway e a Direção de Saúde da Noruega para promover a e-saúde, a integração digital nos países nórdicos e na Alemanha, além de uma cooperação mais forte entre fornecedores e prestadores de serviços de saúde.

Descrição

• As a Site Reliability Engineer on the Central AI team, you will help Health Catalyst engineer teams adopt AI responsibly and effectively. • 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.

🎯 Requisitos

• 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.

🏖️ Benefícios

• flexible PTO • professional development stipend • meaningful opportunities for career growth and development