AI Developer and Engineer

Booz Allen Hamilton•Chantilly, VA

8d•Remote

About The Position

Are you ready to build the infrastructure and systems that power next-generation artificial intelligence? Rapid advances in large language models (LLMs) and intelligent automation are transforming missions across the government and commercial sectors, but these models are only as capable as the architectures supporting them. At Booz Allen, you can apply your systems engineering, infrastructure design, and full-stack development experience to design, deploy, and operationalize high-performance AI systems that integrate seamlessly into complex enterprise client environments. On our team, you’ll architect robust, scalable networks and computing platforms tailored for the demands of modern AI workloads. Using your technical depth and problem-solving drive, you’ll design low-latency data pipelines, configure distributed compute clusters, and bridge cutting-edge LLMs with existing legacy frameworks. You’ll collaborate closely with data scientists, systems engineers, and client stakeholders to evaluate emerging AI technologies, optimize network throughput, and ensure enterprise-grade security and reliability across diverse operational environments. In this role, you’ll help guide a forward-thinking technical team as it delivers high-impact, mission-driven AI capabilities. By balancing innovation with architectural rigor, you will solve critical integration bottlenecks, establish MLOps best practices, and scale intelligent systems that empower clients to make data-driven decisions faster and more effectively. Join us. The world can’t wait.

Requirements

7+ years of experience in software development or systems engineering building, deploying, and maintaining enterprise-grade applications
5+ years of experience supporting offensive or defensive cyber operations roles
Experience integrating modern AI and Large Language Models (LLMs) into existing legacy architectures, APIs, and enterprise systems
Experience with containerization, orchestration, and cluster management platforms, including Docker and Kubernetes
Knowledge of zero-trust architecture, secure API gateways, and data governance controls for AI/LLM deployments
Knowledge of cloud infrastructure services, such as AWS, Azure, or GCP, optimized for AI/ML and distributed processing such as GPU instances, vCPUs, and high-speed networking fabrics
Knowledge of network engineering fundamentals, low-latency protocols, system security, and data pipeline architectures supporting model inference and training
Ability to collaborate with cross-functional technical teams, translate complex client requirements into scalable system designs, and mentor junior engineers
TS/SCI clearance with a polygraph
Bachelor's degree

Nice To Haves

Experience with MLOps/LLMOps tools and continuous integration/continuous deployment (CI/CD) pipelines such as MLflow, Kubeflow, or GitLab CI
Experience working with high-performance networking, software, and hardware
Master’s degree in Computer Science, Artificial Intelligence, or Systems Engineering
Professional certification in Cloud Architecture or DevOps such as AWS Certified Solutions Architect, Azure Solutions Architect Expert, or CKA

Responsibilities

Architect robust, scalable networks and computing platforms tailored for the demands of modern AI workloads.
Design low-latency data pipelines.
Configure distributed compute clusters.
Bridge cutting-edge LLMs with existing legacy frameworks.
Collaborate with data scientists, systems engineers, and client stakeholders to evaluate emerging AI technologies.
Optimize network throughput.
Ensure enterprise-grade security and reliability across diverse operational environments.
Guide a forward-thinking technical team as it delivers high-impact, mission-driven AI capabilities.
Solve critical integration bottlenecks.
Establish MLOps best practices.
Scale intelligent systems that empower clients to make data-driven decisions faster and more effectively.