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About This Role
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.ResponsibilitiesAI/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 TechnologiesLanguages: Python, SQL
AI/ML: Scikit-learn, PyTorch, TensorFlow, Hugging Face
Generative AI: LLMs, RAG, Prompt Engineering, Embeddings, Vector Search, LLM Evaluation
AI Orchestration: LangChain, LlamaIndex, Semantic Kernel or comparable frameworks
Cloud: AWS and/or Azure
Development: REST APIs, Git, CI/CD, Docker
Data: SQL databases, APIs, document repositories, structured and unstructured data
Methodology: 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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Role Details
About This Role
LLM Engineers specialize in building applications powered by large language models. They design RAG systems, fine-tune models, build agent frameworks, and optimize inference pipelines for cost and latency. This is the role that didn't exist three years ago and now has thousands of open positions.
The scope is broad. You might be building a customer support chatbot that needs to pull from a knowledge base of 50,000 documents, or designing an agent that can navigate a company's internal tools to complete multi-step tasks. The common thread is taking a foundation model and making it do something useful, reliably, at scale, without bankrupting the company on API costs.
Across the 9,653 AI roles we're tracking, LLM Engineer positions make up 0% of the market. At REI Systems, this role fits into their broader AI and engineering organization.
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
What the Work Looks Like
A typical week includes: building and testing RAG pipelines (chunking strategies, embedding models, retrieval evaluation), debugging why the agent took a wrong action path, optimizing inference costs (caching, batching, model selection), and working with the product team on new LLM-powered features. You'll context-switch between deep technical work and cross-functional collaboration.
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
Skills Required
RAG and vector databases are the most common requirements. Expect to work with LangChain or LlamaIndex, embedding models, and at least one vector store (Pinecone, Weaviate, Chroma). Python is non-negotiable. Understanding the cost/latency/quality tradeoffs between different model providers and architectures is what separates senior from junior engineers.
Fine-tuning experience is valuable for specific use cases but most production LLM work is RAG-based. Agent frameworks (LangGraph, CrewAI, custom orchestration) are increasingly important as companies move beyond simple chat interfaces. Evaluation and observability tools (LangSmith, Arize, custom dashboards) are essential for production deployments.
Look for roles that specify the production stack, mention specific use cases, and talk about cost optimization. Companies that understand LLM engineering will mention evaluation methodology, latency requirements, and scale targets. Vague 'build AI features' postings often mean they haven't figured out their architecture yet.
Compensation Benchmarks
LLM Engineer roles pay a median of $160,000 based on 33 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
REI Systems AI Hiring
REI Systems has 1 open AI role right now. They're hiring across LLM Engineer. Based in Sterling, VA, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into LLM Engineer roles include Software Engineer, ML Engineer, Data Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, AI Engineering Manager.
The fastest path is through software engineering. If you can build production systems and you understand LLM capabilities and limitations, you're already qualified for most roles. Build a portfolio project that demonstrates RAG implementation, evaluation, and cost optimization. Open-source contributions to LLM frameworks are strong signals to hiring managers.
What to Expect in Interviews
Technical screens cover RAG architecture design, embedding model selection, chunking strategies, and retrieval evaluation. Expect questions about cost optimization: how you'd reduce inference costs by 50% without degrading quality. System design rounds often present scenarios like 'design a customer support chatbot that can access 100K documents' and evaluate your understanding of the full stack from embedding to serving.
When evaluating opportunities: Look for roles that specify the production stack, mention specific use cases, and talk about cost optimization. Companies that understand LLM engineering will mention evaluation methodology, latency requirements, and scale targets. Vague 'build AI features' postings often mean they haven't figured out their architecture yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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