LLNL is looking for a Software Engineer to 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.
Requirements
- 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.
- Familiarity with DevOps, CI/CD pipelines, and containerization (e.g., Docker, Kubernetes).
Responsibilities
- 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.
- Deliver intuitive UIs, dashboards, or endpoints that expose AI functionality to business users.
Other
- Ability to obtain and maintain a U.S. DOE L-level security clearance in the future. This requires U.S. Citizenship.
- This position requires a Department of Energy (DOE) Q-level clearance.
- This position offers a hybrid schedule, blending in-person and virtual presence.
- External applicant(s) selected for this position must pass a post-offer, pre-employment drug test.
- 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.
