AI Engineer with Security Clearance

Key Responsibilities
Develop LLM-powered applications for retrieval, semantic search, summarization, document analysis, and decision support. Build and integrate RAG pipelines, embeddings, vector search, and semantic retrieval capabilities. Develop Python-based APIs and AI application services for production environments. Implement prompt orchestration, context management, evaluation, source citation, and output grounding. Implement guardrails, access controls, authentication, logging, error handling, and output validation.

Evaluate and optimize AI applications for accuracy, relevance, groundedness, latency, and reliability. Integrate AI services with databases, search platforms, APIs, and existing data pipelines. Troubleshoot and optimize LLM, retrieval, API, and AI application workflows. Develop technical documentation for AI architecture, retrieval workflows, APIs, evaluation methods, and operational procedures. Collaborate with AI/ML, data engineering, data science, and software engineering teams throughout development and testing.

Apply security, privacy, accessibility, data governance, and records-management requirements to AI applications. Required Qualifications
Hands-on experience developing applications using LLMs, RAG, semantic search, or generative AI. Experience building AI services for search, summarization, document analysis, question answering, or decision support. Strong Python and API development skills. Experience with embeddings, vector search, retrieval, prompt orchestration, and LLM evaluation. Experience implementing source citation, grounding, traceability, and output validation.

Experience implementing AI guardrails, access controls, logging, monitoring, and error handling. Experience integrating AI/LLM services with databases, APIs, search platforms, and data pipelines. Strong technical communication, documentation, problem-solving, and cross-functional collaboration skills.