Senior AI Architect

LLNL•Livermore, CA

19h•Hybrid

About The Position

Lawrence Livermore National Laboratory (LLNL) is seeking a Senior AI Architect to lead the design, development, deployment, and operationalization of advanced generative AI applications and services. This role will support the National Ignition Facility (NIF) by translating emerging AI technologies into production-ready capabilities. The architect will build backend services, APIs, orchestration layers, and integration patterns to connect AI applications with existing data sources. This is a hybrid position, offering flexibility with some remote work days. The role can be filled at either the SES.3 or SES.4 level, with additional responsibilities assigned at the higher level.

Requirements

Ability to obtain and maintain a US DOE Q-level security clearance which requires U.S. Citizenship.
Bachelor’s degree in a computer or engineering related field, or the equivalent combination of education and related experience.
Significant experience architecting, developing, deploying, and supporting complex software systems, including GenAI-enabled or large language model-based applications, in production or operational environments.
Advanced experience designing and implementing secure, scalable solutions across cloud-based and/or on-premises environments, such as containerized and/or Kubernetes-based platforms.
Significant experience developing backend applications, RESTful APIs, service integrations, and application workflows using languages and frameworks such as Python, Java, JavaScript, or TypeScript.
Advanced knowledge of GenAI application design patterns and techniques, such as prompt engineering, retrieval and grounding strategies, semantic search, evaluation methods, model integration, and safeguards for reliable and context-appropriate responses.
Advanced verbal and written communication skills and a proven ability to lead technical efforts, influence architectural decisions, mentor engineers, and collaborate effectively with customers, developers, infrastructure teams, and management.
Highly advanced experience leading complex, multi team efforts (SES.4 Level).
Expert experience defining architectural direction and platform patterns across cloud-based, on-premises, and containerized environments (SES.4 Level).
Highly advanced experience leading design and implementation of reusable services and integration architectures (SES.4 Level).
Expert experience providing strategic technical leadership and influencing organizational direction (SES.4 Level).

Nice To Haves

Master’s degree in a computer or engineering related field with an emphasis on Machine Learning or Artificial Intelligence.
Experience evaluating and implementing open-source AI frameworks, model-serving platforms, semantic retrieval or vector-based search technologies, and related tooling to support portable AI solutions across cloud and on-premises environments.
Experience designing agentic integration patterns, MCP-enabled services, developer-facing APIs, or related interfaces that support AI assistants, automation tools, and customer-developed applications.
Experience establishing engineering practices for AI systems, including CI/CD, observability, performance tuning, security, and lifecycle management in regulated, mission-critical, or high-availability environments.

Responsibilities

Lead the architecture, design, development, deployment, and lifecycle support of GenAI-enabled applications and services that address mission and operational needs.
Design and implement scalable, secure, and maintainable AI solutions across cloud-based and on-premises infrastructure, including Kubernetes-based environments and open-source or commercially supported AI toolchains.
Evaluate use cases and determine appropriate technical architectures for retrieval-grounded GenAI applications, semantic search, LLM-enabled workflows, RESTful APIs, agentic integration patterns, and related AI services.
Develop and maintain backend services, APIs, orchestration layers, and application integration components that enable AI applications to securely access, transform, and use operational, technical, and knowledge-based data sources.
Drive engineering approaches that improve AI application quality and operational effectiveness, including prompt design, grounding strategies, evaluation methods, observability, performance optimization, cost-aware implementation, and appropriate safeguards.
Serve as a technical leader by guiding architectural decisions, sharing best practices, and helping build broader team capability in AI application development and support.
Partner with customers, domain experts, developers, and infrastructure teams to translate operational needs into production-ready AI solutions and reusable technical capabilities.
Perform other duties as assigned.
Provide technical leadership and strategic direction for departmental GenAI architecture, platform patterns, and integration approaches, ensuring solutions are scalable, secure, reusable, and aligned with long-term organizational needs (SES.4 Level).
Lead complex, cross-functional AI initiatives; make high-impact technical decisions; and serve as a senior advisor to management and engineering teams on GenAI architecture, implementation strategy, platform evolution, and technology selection (SES.4 Level).
Serve as a mentor and help establish best practices in GenAI engineering, API-first design, platform portability, and lifecycle management to strengthen team capability and enable successful delivery across multiple products and projects (SES.4 Level).