Teams at LTS

Design, build, and deploy agentic AI systems, orchestration pipelines, and RAG/knowledge retrieval services. Implement scalable backend services, observability, LLMOps practices, and integrations with code repositories and enterprise systems. Mentor engineers and ensure explainable, traceable AI for regulated federal healthcare environments.

Lead end-to-end front-end development for an enterprise Agentic AI platform. Design interactive, explainable UIs that render streaming LLM outputs, code citations, dependency graphs and authoring workflows. Build real-time experiences with WebSockets/streaming APIs, ensure traceability to source code, collaborate with AI engineers and designers, and drive accessibility, performance, and observability across a FedRAMP-aligned federal deployment.

Design, prototype, and productionize applied AI capabilities (LLMs, RAG, agents) to improve reasoning, explainability, and developer productivity. Optimize retrieval, embeddings, multi-agent workflows, and evaluation frameworks; build benchmarks and diagnostics. Collaborate with architects and engineers to ingest enterprise knowledge, refine semantic search, and transition experiments into FedRAMP-aligned production for federal healthcare modernization.