Job Opportunities API

Location: United States – Remote Clearance: Ability to obtain and maintain a Public Trust LTS is seeking a highly skilled Senior Applied AI Engineer to focus on continuously improving the intelligence behind the platform. You'll experiment with models, optimize retrieval strategies, refine agent reasoning, evaluate AI performance, and transform emerging AI capabilities into production-ready solutions. The Agentic AI platform is designed to help engineers understand, analyze, and modernize one of the most consequential legacy software systems still operating today. Our platform enables engineers to ask questions in plain English and receive explainable, verifiable answers traced directly back to decades of production source code. Rather than replacing engineers, we're building AI that accelerates engineering through transparency, traceability, and intelligent reasoning. We're building an AI-native engineering platform supporting the modernization of mission-critical healthcare systems serving millions of Veterans nationwide. The platform is designed for deployment across federal enterprise environments and is being engineered to align with FedRAMP security controls, Zero Trust principles, and federal compliance requirements. The platform has executive sponsorship, committed users, and a customer investing in long-term modernization. Our engineering team is intentionally small giving every engineer meaningful ownership, and direct influence over product direction. We don't simply build AI-powered software—we build software with AI. This is not another chatbot. Using LLMs, autonomous agents, AI-assisted development, parallel workflows, and model-driven engineering is simply how we work. What You’ll Do: Advance Applied AI Capabilities Design, prototype, and implement production-ready AI capabilities that improve reasoning, accuracy, explainability, and developer productivity. Evaluate emerging LLMs, multimodal models, agent frameworks, and AI techniques to identify opportunities for platform advancement. Rapidly prototype new AI capabilities and transition successful experiments into production. Optimize Agent Performance Improve autonomous and multi-agent workflows through prompt engineering, reasoning optimization, memory strategies, tool selection, and context management. Continuously refine Retrieval-Augmented Generation (RAG) pipelines, retrieval strategies, embeddings, reranking, and grounding techniques. Improve AI response quality through experimentation, benchmarking, and iterative optimization. Evaluate AI Systems Develop evaluation frameworks that measure accuracy, groundedness, explainability, latency, and overall AI effectiveness. Build benchmark datasets, automated evaluation pipelines, and performance metrics for production AI systems. Analyze AI failures, hallucinations, retrieval gaps, and reasoning errors to drive continuous improvement. Knowledge Engineer Collaborate with software engineers to improve knowledge ingestion, document processing, semantic search, embeddings, and enterprise knowledge management. Design approaches that maximize retrieval quality across large technical documentation and source code repositories. Improve how AI agents discover, organize, and reason over enterprise knowledge. Collaborate Across Engineer Partner closely with AI architects, platform engineers, software engineers, and front-end engineers to improve the overall intelligence of the platform. Share research findings, experimental results, and engineering recommendations with cross-functional teams. Help establish best practices for experimentation, evaluation, and AI quality throughout the organization. What We’re Looking For: Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Engineering, Data Science, or a related technical discipline (or equivalent profession