AI Operations Lead

LLNL•Livermore, CA

17h•Hybrid

About The Position

We are seeking a highly qualified AI Operations Lead to lead an interdisciplinary team of systems analysts, computational engineering analysts, and machine learning and artificial intelligence experts. The AI Operations Lead will translate Laboratory operational, policy, and administrative challenges into scalable, data-driven AI workflows and interface with DOE Enterprise-wide AI efforts. You will work with your team to identify opportunities for improving efficiencies in engineering and Laboratory processes and lead your team in the development and integration of artificial intelligence tools to help drive measurable improvements. You and your team will work to address challenges across safety design and analysis, driving efficiencies in administrative process and procedures related to manufacturing, automating analysis workflows, knowledge management and training of domain specialists. Qualified candidates should have in-depth knowledge of machine learning and artificial intelligence models and toolkits, demonstrated experience in applied research in machine learning, or a complementary scientific discipline providing underlying skills in data analytic techniques, strong understanding of data engineering, excellent organizational and communication skills, and have experience with DOE/NNSA administrative policies and procedures. This position is in the Computational Engineering Division (CED) within the Engineering Directorate, and you will be supporting programs in the Office of the Director. In this role, you will Serve as a trusted advisor on applied AI for operational challenges and lead Laboratory-wide and interface with DOE Enterprise-wide applied AI efforts for Laboratory operational applications. Translate Lab operational, policy, and administrative challenges into scalable, data-driven AI workflows and communicate technical concepts and tradeoffs clearly to non-technical stakeholders. Define technical roadmaps for AI adoption aligned with mission, security, and enterprise IT constraints. Build, staff, and lead project teams across a diverse portfolio of operational AI applications including working with discipline organization across the Laboratory to source talent and assemble matrixed, high performing, cross functional teams. Provide technical leadership, mentorship, and oversight for AI engineers, data scientists, and software developers. Design and deploy AI-enabled workflows onto enterprise IT infrastructure in coordination with stakeholders. Produce plans for software quality assurance, as applicable. Maintain and evolve multiple codebases supporting operational use cases. Oversee development, integration, testing, and sustainment of production AI systems, and maintenance of AI tools at multiple classification levels, ensuring secure and reliable access to frontier AI models and other machine learning tools across networks and teams. Drive best practices for MLOps, model lifecycle management, and system reliability. Partner with Lab policy offices, program organizations, external mission stakeholders, and LivIT leadership to align solutions with mission needs and constraints - developing mitigations and solutions to challenges with Lab leadership as appropriate. This includes working with applicable teams to ensure compliance with cybersecurity, data governance, and classification requirements. Perform other duties as assigned.

Requirements

Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship.
Bachelor’s degree in Computer Science, Computational Engineering, Applied Statistics, Applied Mathematics or related field, or the equivalent combination of education and related experience.
Substantial experience and subject matter expert knowledge in at least one of the following core technology areas: artificial intelligence and machine learning, machine learning operations, data engineering, data management and governance, development operations, or software engineering.
Substantial experience in researching, writing, and presenting successful technical proposals to senior management and internal and external sponsors.
Expert communication, facilitation, and collaboration skills necessary to effectively present, explain, influence and advise senior management, programmatic organizations, and/or sponsors both internal and external.
Substantial experience providing technical leadership and working in a multidisciplinary team environment including experience in all phases of project management.
Demonstrated ability to lead others through change in program directions and related policies.
Demonstrated ability to provide technical guidance, solutions, strategies, and communications that influences the organization across all levels.
Ability to travel off-site for sponsor and customer interaction.

Nice To Haves

PhD in Computer Science, Computational Engineering, Applied Statistics, Applied Mathematics or the equivalent combination of education and related experience.
Ability to obtain and maintain Sensitive Compartmented Information (SCI) access which requires U.S. citizenship.
Experience specifying data requirements and quality control for artificial intelligence and machine learning applications.
Substantial knowledge and significant experience with the implementation of DOE, NNSA, and Laboratory operational policies and procedures.

Responsibilities

Serve as a trusted advisor on applied AI for operational challenges and lead Laboratory-wide and interface with DOE Enterprise-wide applied AI efforts for Laboratory operational applications.
Translate Lab operational, policy, and administrative challenges into scalable, data-driven AI workflows and communicate technical concepts and tradeoffs clearly to non-technical stakeholders.
Define technical roadmaps for AI adoption aligned with mission, security, and enterprise IT constraints.
Build, staff, and lead project teams across a diverse portfolio of operational AI applications including working with discipline organization across the Laboratory to source talent and assemble matrixed, high performing, cross functional teams.
Provide technical leadership, mentorship, and oversight for AI engineers, data scientists, and software developers.
Design and deploy AI-enabled workflows onto enterprise IT infrastructure in coordination with stakeholders.
Produce plans for software quality assurance, as applicable.
Maintain and evolve multiple codebases supporting operational use cases.
Oversee development, integration, testing, and sustainment of production AI systems, and maintenance of AI tools at multiple classification levels, ensuring secure and reliable access to frontier AI models and other machine learning tools across networks and teams.
Drive best practices for MLOps, model lifecycle management, and system reliability.
Partner with Lab policy offices, program organizations, external mission stakeholders, and LivIT leadership to align solutions with mission needs and constraints - developing mitigations and solutions to challenges with Lab leadership as appropriate. This includes working with applicable teams to ensure compliance with cybersecurity, data governance, and classification requirements.
Perform other duties as assigned.