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 retraining pipelines.
- Track model and prompt versions, evaluation results, configurations, dependencies, and production behavior.
- Monitor model quality, retrieval quality, latency, cost, drift, failures, and other operational metrics and recommend improvements.
- Participate in CI/CD processes for AI/ML applications and containerize services using technologies such as Docker.
- Collaborate with DevOps and cloud engineering teams to deploy scalable, observable, and resilient AI solutions.
Responsible AI, Security & Governance
- Apply responsible AI practices including transparency, traceability, evaluation, testing, documentation, and appropriate human oversight.
- Evaluate AI solutions for bias, accuracy, reliability, privacy, security, data leakage, prompt injection, and other relevant risks.
- Implement safeguards appropriate to the use case, including input/output controls, content filtering, grounding checks, access controls, and auditability.
- Follow applicable federal and FDA security, privacy, data-governance, records-management, and software development requirements.
- Maintain technical documentation covering models, prompts, data sources, evaluation methods, configurations, limitations, testing, and deployment.
- Participate in technical and governance reviews and provide documentation required for production deployment and operational support.
Software Engineering & Agile Delivery
- Develop clean, maintainable, testable, and reusable Python code following established software engineering standards.
- Develop RESTful APIs and backend services that expose AI/ML functionality to enterprise applications.
- Participate in code reviews, unit testing, integration testing, technical design discussions, and documentation.
- Work within Agile/Scrum development teams and participate in sprint planning, backlog refinement, demonstrations, and retrospectives.
- Collaborate with application developers to integrate AI functionality into Java, .NET, web, and cloud-based enterprise applications without assuming ownership of the full application stack.
Qualifications
- Approximately 5-8 years of professional software development, data engineering, data science, AI/ML, or related technical experience, including substantial hands-on AI/ML development experience.
- Strong programming skills in Python and experience building production-quality Python applications or services.
- Hands-on experience with machine learning libraries/frameworks such as Scikit-learn, PyTorch, TensorFlow, Hugging Face, or similar technologies.
- Experience developing, evaluating, and integrating AI/ML models into applications or production environments.
- Hands-on experience with Generative AI, LLMs, NLP, and LLM APIs.
- Strong understanding of RAG, embeddings, vector search, prompt engineering, grounding, context management, and LLM evaluation.
- Experience working with structured and unstructured data, including preprocessing, transformation, validation, and analysis.
- Experience developing REST APIs and backend services for AI/ML functionality.
- Working knowledge of SQL and relational databases.
- Familiarity with Git, CI/CD, automated testing, containerization, and modern software development practices.
- Experience working in Agile, multidisciplinary development environments.
- Strong analytical, experimentation, troubleshooting, and problem-solving skills.
- Ability to communicate AI/ML concepts, limitations, risks, and recommendations clearly to technical and non-technical stakeholders.
Preferred Qualifications
- Experience supporting FDA, HHS, or other federal government programs.
- Experience developing and deploying AI/ML solutions in AWS or Azure.
- Experience with AWS AI/ML services such as Amazon Bedrock, SageMaker, Lambda, S3, OpenSearch, or related services.
- Experience with Azure AI services or Azure OpenAI.
- Experience with vector databases or vector search technologies such as Pinecone, FAISS, OpenSearch, pgvector, Chroma, or similar technologies.
- Experience with LangChain, LlamaIndex, Semantic Kernel, or comparable AI orchestration frameworks.
- Experience developing document intelligence, NLP, semantic search, knowledge-management, or enterprise search solutions.
- Experience designing automated LLM evaluation, prompt/version management, guardrails, or LLMOps capabilities.
- Experience with Docker and containerized AI application deployment; familiarity with Kubernetes or cloud-native environments.
- Experience with MLOps, model monitoring, model versioning, experiment tracking, or ML lifecycle management.
- Understanding of microservices, event-driven integration, and enterprise application architecture.
- Familiarity with DevSecOps practices and secure software development for AI-enabled systems.
- Experience working with sensitive, regulated, scientific, healthcare, or federal datasets is a plus.
Professional Skills
- Strong analytical and critical-thinking abilities.
- Excellent written and verbal communication skills.
- Ability to translate business requirements into practical AI/ML and LLM-enabled solutions.
- Ability to independently own complex AI development tasks while collaborating effectively within a larger technical team.
- Strong attention to detail and commitment to model, software, evaluation, and data quality.
- Ability to balance experimentation with production engineering, security, governance, performance, and operational constraints.
- Ability to communicate technical risks, assumptions, model limitations, dependencies, and recommendations clearly.
- Strong documentation, mentoring, and knowledge-sharing skills.
Key Technologies
Languages: Python, SQLAI/ML: Scikit-learn, PyTorch, TensorFlow, Hugging FaceGenerative AI: LLMs, RAG, Prompt Engineering, Embeddings, Vector Search, LLM EvaluationAI Orchestration: LangChain, LlamaIndex, Semantic Kernel or comparable frameworksCloud: AWS and/or AzureDevelopment: REST APIs, Git, CI/CD, DockerData: SQL databases, APIs, document repositories, structured and unstructured dataMethodology: Agile/Scrum, MLOps/LLMOps, DevSecOps
Ideal Candidate Profile
The successful candidate will be a hands-on AI/ML and LLM engineer rather than solely a researcher, data analyst, or AI strategist. They should be comfortable writing production-quality Python, working with data, building and evaluating ML/LLM solutions, implementing RAG pipelines, developing AI APIs, and collaborating with application and cloud engineering teams to move AI capabilities from prototype into secure production environments.
This individual should have sufficient AI/ML experience to independently own complex development and evaluation tasks, make sound implementation recommendations, contribute to architecture and technical reviews, and mentor less-experienced engineers while working under the broader direction of senior architects and program leadership.
Education: Bachelor’s degree in Computer Science, Data Science, or a related field; Master’s degree preferred.
Location: Hybrid - Sterling, VA HQ (with flexibility for remote work as per company policy).
Clearance: Candidate must be able to obtain and maintain a Clearance.
EEO Statement: REI Systems is an Equal Opportunity Employer
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