Scientific AI Solutions Architect

Serve as a subject matter expert in the design, implementation, enhancement, and maintenance of directorate-wide, AI-enabled solutions that streamline experimental data categorization, processing, and storage. Partner closely with scientific domain experts to develop solutions that prepare collected data for downstream AI applications, including model training and related analytical workflows.

This role requires a strong understanding of data structure, provenance, labeling, and intended processing methods. The successful candidate will establish and maintain regular written and in-person communication with scientific collaborators, cybersecurity staff, central IT, and team members regarding technical issues, system updates, and planned changes.

In addition, this position will lead significant projects and initiatives focused on transforming architected workflows into enterprise-class solutions that meet Argonne's operational, technical, and security requirements.

• Experience analyzing, designing, implementing, and maintaining AI workflows using on-premises tools such as Dify
• Knowledge of large language models (LLMs), including model selection and training approaches for localized or on-premises deployments
• Expertise in prompt engineering, refinement, and optimization to improve efficiency and effective token/resource usage
• Strong understanding of the software development lifecycle, including scripting, documentation, version control, and sound coding practices
• Ability to use and operate LLMs while safeguarding sensitive and proprietary information
• Strong oral, written, and interpersonal communication skills, with the ability to work effectively with peers, staff, and all levels of management
• Ability to learn and follow Laboratory and Department of Energy policies and procedures
• Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork

Desired Knowledge, Skills, and Experience

• Knowledge of automation tools and core technology principles
• Familiarity with Globus, including Data Transfer Nodes (DTNs), storage gateways, and collections
• Knowledge of object storage technologies, including Amazon S3-compatible platforms, and familiarity with the HDF5 file format
• Ability to quickly learn and adapt to new technologies and techniques in a rapidly evolving market
• Ability to work effectively in a fast-paced environment with changing priorities
• Strong project management and time management skills, with the ability to work independently

PT2: Bachelors and 2+ years of experience, or equivalent

Join Argonne National Laboratory and help shape the future of AI-enabled scientific data workflows. We are seeking a technically strong and collaborative professional to support the design, implementation, and advancement of enterprise-class AI solutions that improve how experimental data is categorized, processed, and stored. This is an opportunity to work at the intersection of science, technology, and operations while partnering with experts across research, cybersecurity, and IT to deliver impactful solutions that meet mission and institutional needs., Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department. All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.