AI/ML Engineer

Job description

**Closure Technologies** is seeking a **AI/ML Engineer** who will Implement and maintain Retrieval-Augmented Generation (RAG) pipelines and integrate Large Language Models (LLMs) into applications, supported by API development and optimizing data storage through Postgres schema refinement. **Clearance Requirement:** TS/SCI with Polygraph **Key Responsibilities:** - Implement and maintain RAG pipelines, including document processing, embedding generation, retrieval configuration, and prompt assembly. - Integrate LLMs into applications using available APIs and frameworks. - Develop and maintain REST API interactions to support data retrieval and system integration. - Design or refine Postgres schemas to improve data organization and query performance. **Required Qualifications:** - Demonstrated ability to conduct independent technical research, evaluate emerging AI/ML approaches, and apply advanced analytical problem-solving comparable to PhD-level research environments. - Ability to rapidly learn and apply new AI/ML methodologies, tools, and frameworks in support of evolving mission requirements. - Experience developing AI/ML applications focused on Retrieval-Augmented Generation (RAG), semantic retrieval, LLM integration, or related AI workflows. - Strong proficiency in Python and modern AI/ML libraries, frameworks, and API integrations. - Active/current TS/SCI with required polygraph. - Willingness to work onsite full time. - US citizenship required. - Senior Labor Category: Minimum 8 years of experience with a Bachelor’s degree; or 7 years of experience with a Masters degree; or 6 years of experience with a Doctorate **Preferred Qualifications:** - Advanced research experience in machine learning, deep learning, natural language processing, generative AI, reinforcement learning, computer vision, or related disciplines. - Experience publishing research, contributing to open-source AI/ML initiatives, or leading experimental and prototype development efforts. - Familiarity with model evaluation frameworks, fine-tuning workflows, inference optimization, and AI observability/monitoring tools. - Experience with vector databases, AWS/cloud environments, Docker, and containerized AI/ML development workflows. - Experience designing and integrating REST APIs and scalable data architectures.