AI Model SME

Booz Allen Hamilton•Fort Meade, MD

1d•$99,000 - $225,000•Onsite

About The Position

As an experienced engineer, you understand that modern mission systems depend on the ability to develop, optimize, and deploy AI/ML models that can operate reliably at scale. Your expertise in analyzing datasets, engineering features, and applying advanced statistical and machine learning techniques will be critical to ensuring model readiness and enhancing real-world operational outcomes. We need your technical depth and problem‑solving mindset to advance the development, deployment, and continuous improvement of AI models that directly support evolving mission needs. As an AI Model SME on our team, you’ll design, test, optimize, and operationalize models across cloud, edge, and hybrid environments. You will drive the technical direction for mission‑critical AI solutions by selecting best‑fit architectures, implementing robust deployment pipelines, and integrating models with enterprise and mission systems. You’ll collaborate closely with data engineers, data scientists, cloud and network architects, and ISSE partners to ensure models are supported by secure, scalable infrastructure and high‑quality data pipelines. You’ll also contribute technical leadership by evaluating emerging AI/ML frameworks, guiding feature engineering approaches, supporting fine‑tuning and retraining cycles, and implementing monitoring frameworks to detect performance drift. Your expertise will help mission partners understand and navigate the rapidly evolving landscape of AI/ML technologies, ensuring deployed models remain performant, reliable, and aligned with operational objectives. Work with us to solve complex real‑world challenges and shape the model lifecycle strategy that will drive innovation across mission environments. Join us. The world can’t wait.

Requirements

6+ years of experience with engineering
Experience developing, training, validating, and deploying AI/ML models across cloud, edge, or hybrid environments
Experience with data preprocessing, feature engineering, and exploratory data analysis
Experience optimizing AI/ML models including compression, pruning, quantization, and distributed training
Experience with computing, storage, and networking requirements across model training, tuning, and inference phases
Knowledge of statistical modeling, machine learning workflows, and model evaluation techniques
Ability to design and implement model deployment pipelines and MLOps workflows
Ability to implement monitoring frameworks for detecting model drift, performance degradation, and operational issues
TS/SCI clearance with a polygraph
HS diploma or GED

Nice To Haves

Experience with distributed compute frameworks such as Ray, Spark, or Horovod for large-scale model training
Experience with cloud-native AI platforms including AWS Sagemaker, Azure ML, or GCP Vertex AI
Experience deploying AI/ML models to edge devices or tactical and operational environments
Experience with common ML frameworks such as PyTorch, TensorFlow, or Scikit-learn
Experience working in mission-focused or national security environments
Experience with vector databases, data lakehouse architectures, or streaming data systems
Experience evaluating new AI model architectures such as LLMs or diffusion models and conducting model benchmarking
Knowledge of secure AI engineering best practices such as adversarial robustness, model governance, or NIST AI RMF
Possession of strong programming skills in languages such as Python
Bachelor’s degree

Responsibilities

Design, test, optimize, and operationalize AI/ML models across cloud, edge, and hybrid environments.
Drive the technical direction for mission-critical AI solutions by selecting best-fit architectures, implementing robust deployment pipelines, and integrating models with enterprise and mission systems.
Collaborate closely with data engineers, data scientists, cloud and network architects, and ISSE partners to ensure models are supported by secure, scalable infrastructure and high-quality data pipelines.
Evaluate emerging AI/ML frameworks, guide feature engineering approaches, support fine-tuning and retraining cycles, and implement monitoring frameworks to detect performance drift.
Help mission partners understand and navigate the rapidly evolving landscape of AI/ML technologies, ensuring deployed models remain performant, reliable, and aligned with operational objectives.
Solve complex real-world challenges and shape the model lifecycle strategy that will drive innovation across mission environments.