Senior AI Architect
$175,530–$267,060 year
Hybrid · Livermore, California, United States
Job Summary
Lead the architecture, design, deployment, and lifecycle support of GenAI-enabled applications and services addressing mission and operational needs at LLNL. Define scalable, secure AI solutions across cloud and on-premises infrastructure (Kubernetes-based) with API-first integration, retrieval-grounded GenAI, semantic search, and LLM-enabled workflows. Develop backend services, orchestration layers, and data integrations to securely transform and utilize operational, technical, and knowledge-based sources. Drive engineering practices including prompt design, grounding strategies, observability, performance optimization, and safeguards. Provide technical leadership, mentorship, and collaboration with customers, domain experts, developers, and infrastructure teams to deliver production-ready AI capabilities. Additional SES.4 responsibilities include strategic GenAI architecture direction, cross-functional initiatives, and enabling teams with reusable services and platform patterns.
Required Qualifications
- Bachelor’s degree in computer science, engineering, or related field (or equivalent combination of education and experience)
- Significant experience architecting, developing, deploying, and supporting GenAI-enabled or large language model-based applications in production environments
- Advanced experience designing secure, scalable solutions across cloud-based and/or on-premises environments (containerized/Kubernetes)
- Significant experience developing backend applications, RESTful APIs, and service integrations using Python, Java, JavaScript, or TypeScript
- Advanced knowledge of GenAI design patterns (prompt engineering, retrieval/grounding, semantic search, evaluation, safeguards)
- Strong communication and leadership skills; ability to influence architectural decisions and mentor engineers
- Ability to obtain and maintain a DOE Q-level security clearance (U.S. citizenship)
- Experience working with customers, domain experts, developers, and infrastructure teams to translate operational needs into production-ready AI solutions
- Experience implementing observability, performance tuning, and lifecycle management in regulated/high-availability environments
- Relocation assistance experience or willingness to relocate
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