Senior AI Architect

Join us and make YOUR mark on the World! Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability. Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact. We have an opening for a Senior AI Architect to lead the design, development, deployment, and operationalization of advanced generative AI applications and services that support mission and operational need within the National Ignition Facility. You will help translate emerging AI technologies into production-ready capabilities by building backend services, APIs, orchestration layers, and integration patterns that enable AI applications to interact with existing operations, technical, and knowledge-based data sources. This position is in the National Ignition Facility Computing (NIFC) Division within the Computing Directorate. This position offers a hybrid schedule, blending in-person and virtual presence. You will have the flexibility to work from home one or more days per week. This position will be filled at either the SES.3 or SES.4 level based on knowledge and related experience as assessed by the hiring team. Additional job responsibilities (outlined below) will be assigned if hired at the higher level. You will 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. Additional responsibilities at the SES.4 Level 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. 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. 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. 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. Additional qualifications at the SES.4 level Highly advanced experience leading complex, multi team efforts. Expert experience defining architectural direction and platform patterns across cloud-based, on-premises, and containerized environments. Highly advanced experience leading design and implementation of reusable services and integration architectures. Expert experience providing strategic technical leadership and influencing organizational direction. Qualifications We Desire 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. Pay Range $175,530 - $267,060 Annually $175,530 - $222,564 Annually for the SES.3 level $210.630 - $267,060 Annually for the SES.4 level This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs. #LI-Hybrid Position Information This is a Career Indefinite position, open to Lab employees and external candidates. Why Lawrence Livermore National Laboratory? Included in 2026 Best Places to Work by Glassdoor! Flexible Benefits Package 401(k) Relocation Assistance Education Reimbursement Program Flexible schedules (*depending on project needs) Our values - visit https://www.llnl.gov/inclusion/our-values Security Clearance This position requires a Department of Energy (DOE) Q-level clearance. If you are selected, we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. Also, all L or Q cleared employees are subject to random drug testing. Q-level clearance requires U.S. citizenship. Pre-Employment Drug Test External applicant(s) selected for this position must pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor. Wireless and Medical Devices Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the use and/or possession of mobile devices in Limited Areas. 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