AI/ML & LLM Engineer

REI Systems•Sterling, VA

5d•Hybrid

About The Position

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.

Requirements

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.
Bachelor’s degree in Computer Science, Data Science, or a related field.

Nice To Haves

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.
Master’s degree preferred.

Responsibilities

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.
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.
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.
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.
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.
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.