Principal AI 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  Employees voted REI Systems a Washington Post Top Workplace in 2015, 2016, 2018, 2020, 2021, 2022, 2023, 2024, and 2025!

Responsibilities

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).

Key Responsibilities and Activities

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.

AI/ML Model Development

* 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 Lang Chain/Lang Graph.

* 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.

Data Preparation and Integration

* 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.

Cloud Integration and Deployment

* 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…