Principal AI Engineer

REI Systems•Sterling, VA

15h•Hybrid

About The Position

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! Position Overview: REI is advancing Artificial Intelligence to modernize federal digital platforms, improve decision-making, and accelerate mission outcomes through intelligent automation, data-driven solutions, and AI-enabled enterprise architectures. The Principal AI Engineer provides strategic and hands-on technical leadership to integrate AI across cloud modernization, enterprise platforms, and digital transformation initiatives. This role guides agencies in adopting secure, scalable, and responsible AI capabilities aligned with federal compliance and mission needs. Operating in an AI-enabled modernization environment, this leader ensures AI solutions enhance operational efficiency, strengthen system interoperability, and deliver measurable mission impact across federal programs supporting both growth (innovation, solutioning, and client pursuit support) and delivery (end-to-end implementation and operationalization).

Requirements

8+ years of total experience, with at least 4+ years in AI/ML solution design and implementation and demonstrated GenAI/LLM delivery (preferred).
Proven track record of working with cloud-based AI/ML platforms (AWS, Azure, GCP), including production deployments and operational support.
Proficiency in Python and experience with ML frameworks such as PyTorch, Scikit-learn, Hugging Face, and LangChain/LangGraph.
Strong knowledge of data preprocessing, feature engineering, and model evaluation techniques, including LLM/agent evaluation.
Experience with cloud AI/ML services such as AWS SageMaker, AWS Bedrock, Azure ML / Azure AI Studio, or Vertex AI.
Familiarity with big data platforms like Snowflake and Databricks.
Experience with RAG architectures, Knowledge Graphs, Vector Search, and agent orchestration/tooling (e.g., LangChain/LlamaIndex/Semantic Kernel or similar).
Experience with MLOps/LLMOps tooling (e.g., MLflow, model registries, CI/CD, monitoring/observability) is a strong plus.
Understanding of low-code platforms with AI capabilities, such as Salesforce and Appian, is a plus.
Strong problem-solving abilities and critical thinking skills.
Excellent communication and interpersonal skills, with the ability to articulate technical concepts to non-technical audiences.
Adaptability and eagerness to stay updated with evolving AI/ML technologies.
Ability to obtain and maintain relevant security clearances.
Bachelor’s degree in Computer Science, Data Science, or a related field; Master’s degree preferred.

Nice To Haves

AI/ML certifications (e.g., AWS Machine Learning Specialty, AWS Generative AI, Azure AI Engineer).
Experience working with Federal agencies or large-scale enterprises.
Familiarity with ethical AI principles and responsible AI practices, including governance, model risk considerations, and auditability.

Responsibilities

Solution Design and Implementation Architect and design scalable AI/ML solutions using cloud platforms such as AWS, Azure, and Google Cloud Vertex AI.
Collaborate with cross-functional teams to gather requirements, assess feasibility, and define technical specifications for AI projects.
Develop and implement AI-driven prototypes and PoCs to demonstrate the value of AI to clients, including GenAI/RAG and agentic workflow demonstrations where applicable.
Define target architectures for LLMOps/MLOps, including model lifecycle management, evaluation, deployment patterns, and security controls.
Apply core data science techniques, including data preprocessing, feature engineering, and model selection.
Build, train, deploy and optimize machine learning models using PyTorch, Hugging Face, Scikit-learn, and LangChain/LangGraph.
Ensure AI/ML solutions meet performance, accuracy, security, privacy, and interpretability standards.
Experience with generative AI models and methodologies is a strong plus, including prompt engineering, fine-tuning, embeddings, RAG, evaluation harnesses, and safety/guardrails.
Implement agentic AI patterns such as tool/function calling, planner-executor flows, structured output, human-in-the-loop, and multi-step reasoning workflows.
Analyze and preprocess structured and unstructured data for AI model development.
Collaborate with data engineers to design and optimize data pipelines for AI workflows.
Address data quality, completeness, and bias issues to ensure robust AI outcomes.
Leverage platforms like Databricks and Snowflakes for data processing and AI solution integration.
Implement modern unstructured data pipelines including document ingestion, chunking, metadata enrichment, embedding generation, and vector search using vector databases/indexes, and Knowledge Graphs.
Utilize cloud-native AI/ML services, including managed AI services, AutoML, and serverless architectures, plus managed GenAI services (e.g., AWS Bedrock, Azure AI Studio/Azure Machine Learning, Vertex AI).
Deploy AI models and solutions in cloud environments, ensuring scalability and performance optimization using containers/Kubernetes, CI/CD, and infrastructure-as-code.
Monitor and maintain deployed models, iterating as necessary based on feedback and performance metrics, including model/agent observability, drift detection, cost controls, and quality regression testing.
Implement efficient LLM serving and integration patterns (as needed) and API gateway/zero-trust approaches.
Partner with internal account teams across all Business Units to support pre-sales and sales activities by presenting AI solutions to Federal clients, including client workshops, solutioning, whiteboarding, proposal support, and technical volume contributions.
Collaborate with clients and stakeholders to align AI solutions with business goals and regulatory requirements.
Provide technical leadership and mentorship to junior team members and account team members on AI/ML methodologies, GenAI/agentic AI patterns, and best practices.
Understand and incorporate AI features of low-code platforms like Salesforce and Appian into AI/ML solutions where applicable, and similar enterprise platforms as needed.
Advise on integrating low-code and AI capabilities to enhance application development processes, solution efficiency and effectiveness.
Stay up-to date on advancements in AI technologies and solutions, including agent frameworks, evaluation approaches, and responsible AI governance.