Company Description

Join us and make YOUR mark on the World!

Are you interested in joining some of the brightest talent in the world to strengthen the United States’ security? Come join Lawrence Livermore National Laboratory (LLNL) where our employees apply their expertise to create solutions for BIG ideas that make our world a better place.

We are dedicated to fostering a culture that values individuals, talents, partnerships, ideas, experiences, and different perspectives, recognizing their importance to the continued success of the Laboratory’s mission.

Pay Range

$168,780 - $214,032 annually

Job Description

We're looking for a mid-level Software Engineer to join our Enterprise Data Management team supporting AI enablement. In this role, you will help design, develop, and deploy solutions that integrate Retrieval-Augmented Generation (RAG) pipelines and data virtualization technologies to empower AI-driven decision-making across critical business functions including Finance, HR, Procurement, Environmental, Safety & Health, and Facilities and Infrastructure.

You will work closely with application developers, data scientists, product owners, and business analysts to ensure that high-quality, context-rich data is accessible and usable by AI applications—helping to unlock business insights and operational efficiency.

*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.*

You will

• Build and maintain Retrieval-Augmented Generation pipelines, integrating LLMs (e.g., OpenAI, Anthropic, etc.) with enterprise document stores and vector databases.
• Develop scalable, secure APIs and microservices that enable RAG-based applications (e.g., AI copilots, intelligent document search).
• Work with application development teams to optimize retrieval performance and improve accuracy through prompt engineering and grounding techniques.
• Collaborate with data engineering teams to integrate virtualized data sources (e.g., via Denodo) into AI workflows.
• Build connectors and middleware to access and transform real-time operational data for consumption by LLMs and analytics services.
• Ensure solutions maintain data lineage, access controls, and governance policies.
• Partner with application developers and stakeholders across operational areas such as Finance, HR, and Procurement to identify AI opportunities and build tools that reduce manual workflows (e.g., invoice summarization, policy Q&A, contract analysis).
• Deliver intuitive UIs, dashboards, or endpoints that expose AI functionality to business users.
• Monitor system performance and iterate on feedback to improve usability, explainability, and relevance.
• Perform duties as assigned

Qualifications

• Ability to obtain and maintain a U.S. DOE L-level security clearance in the future. This requires U.S. Citizenship.
• Bachelor’s degree in Computer Science, Engineering, or related technical field.
• Significant experience in software development, preferably in enterprise or data-rich environments.
• Advanced Python, JavaScript/TypeScript, or Java experience for backend and integration development.
• Significant experience building AI-powered applications, particularly using RAG architectures, vector databases (e.g., OpenSearch, pgvector, Pinecone), and LLM APIs (e.g., OpenAI, Azure OpenAI).
• Significant experience with data virtualization tools (Denodo) or similar data integration platforms.
• Significant experience in leveraging RESTful API development, microservices, and cloud platforms (e.g., AWS, Azure).
• Advanced knowledge of data privacy, security best practices, and compliance when working with operational data.

Qualifications We Desire

• Experience working within Agile/Scrum environments supporting cross-functional development teams
• Knowledge of enterprise data systems (e.g., Oracle Finance, Oracle HCM Cloud, PeopleSoft, Hexagon/Infor EAM).
• Exposure to vector embeddings, semantic search, and knowledge graph technologies.
• Familiarity with DevOps, CI/CD pipelines, and containerization (e.g., Docker, Kubernetes).
• Advanced knowledge of prompt tuning, feedback loops, and human-in-the-loop AI design.

Additional Information

#LI-Hybrid

Position Information

This is a Career Indefinite position, open to Lab employees and external candidates.

Why Lawrence Livermore National Laboratory?

• Included in 2025 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/diversity/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. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.

If you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities require separate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings.