AI/ML & LLM Engineer

Overview REI Systems' mission is to deliver reliable, innovative technology solutions that advance Federal clients' missions and exceed their expectations. Our technologists and consultants are passionate about solving complex challenges that impact millions of lives. We take a Mindful Modernization® approach in delivering our services, including application modernization, grants management, case management systems, government data analytics, and advisory services. This approach, the REI Way, ensures mission impact by aligning our clients' strategic objectives with measurable outcomes through people, processes, and technology. We offer the same commitment to our employees by providing professional development, meaningful projects, and flexibility to spend time with family and friends. We believe employees are at their best when fulfilled in both their professional careers and their personal lives. Learn more at www.REIsystems.com. Employees voted REI Systems a Washington Post Top Workplace in 2015, 2016, 2018, 2020, 2021, 2022, 2023, 2024, and 2025! Responsibilities Position Overview REI Systems is seeking a mid-senior AI/ML & LLM Engineer to support technology modernization and digital transformation initiatives for the FDA. The AI/ML & LLM Engineer will design, develop, evaluate, deploy, and support machine learning and Generative AI solutions that improve data analysis, automate business processes, enhance search and knowledge discovery, and support intelligent decision-making. The ideal candidate combines strong Python, machine learning, and LLM engineering skills with practical experience building production-oriented RAG, NLP, and AI services. This role is intended for a hands-on engineer who can independently own complex AI development tasks while collaborating with data engineers, software developers, product owners, architects, cloud engineers, and business stakeholders. Responsibilities AI/ML Development Design, develop, test, and implement machine learning and AI solutions to address business and operational requirements. Develop machine learning models using structured, semi-structured, and unstructured datasets. Build and maintain reusable Python components, APIs, services, notebooks, and AI/ML pipelines. Perform data preparation, feature engineering, model training, validation, testing, performance evaluation, and optimization. Evaluate algorithms, models, and approaches based on accuracy, robustness, performance, scalability, explainability, and business requirements. Integrate AI/ML capabilities into existing enterprise applications and workflows through well-defined services and APIs. Develop proof-of-concepts and prototypes and transition successful solutions into secure, production-ready capabilities. Troubleshoot model, data, integration, latency, and application issues throughout the development lifecycle. Generative AI & LLM Solutions Develop applications leveraging Large Language Models (LLMs) and Generative AI technologies. Build and support Retrieval-Augmented Generation (RAG) solutions using enterprise documents, structured data, and approved knowledge sources. Implement prompt engineering, prompt templates, grounding, context management, tool/function calling, and structured LLM outputs. Integrate commercial and/or open-source LLMs through APIs and enterprise AI platforms. Work with embeddings, semantic search, vector databases, reranking, chunking strategies, and document-processing pipelines. Develop AI-enabled capabilities such as intelligent search, summarization, classification, information extraction, question-answering, and workflow automation. Design and execute LLM evaluation approaches for accuracy, relevance, groundedness, consistency, safety, latency, and potential hallucinations. Implement appropriate guardrails, monitoring, fallback strategies, and human-in-the-loop processes for Generative AI applications. RAG, Knowledge & Data Engineering Design ingestion and retrieval pipelines for enterprise documents and data sources used by AI/LLM applications. Develop data preprocessing, cleansing, transformation, chunking, metadata enrichment, and validation routines. Work with relational databases, APIs, document repositories, object storage, search platforms, and cloud-based data services. Write and optimize SQL queries and retrieval logic to support model training, evaluation, and inference. Collaborate with data engineers to establish reliable, governed data pipelines for AI/ML use cases. Ensure appropriate handling of data quality, lineage, security, access controls, and source traceability. MLOps, LLMOps & Deployment Deploy AI/ML models and LLM-enabled services into development, test, and production environments. Develop and maintain model inference APIs, AI services, and microservices using production software engineering practices. Build or support automated model testing, evaluation, deployment, monitoring, and re