GPU-Powered AI Infrastructure Engineer

Phase2 Technology; Corridor North (MD); On-site; USD 112,800 - 257,000; Full time

Job summary

Booz Allen Hamilton is seeking an AI Integration Engineer to design, deploy, and maintain the AI infrastructure that supports LLMs and distributed AI workloads. You will bridge advanced AI models with compute infrastructure and operational workflows to ensure stable deployments.

Ideal candidates have a strong background in high-performance computing, cloud infrastructure, MLOps or DevOps, and AI ecosystem integration.

Qualifications

• Bachelor’s degree in CS, Computer Engineering, or Systems Engineering is required.
• 5+ years in infrastructure engineering or system integration roles.
• 2+ years supporting large-scale AI/ML systems or GPU-centric environments.

Responsibilities

• Serve as the technical point of contact for integrating LLMs and other AI workloads across infrastructure systems and pipelines.
• Architect, deploy, and maintain scalable GPU computing environments for autonomous workflows.
• Develop, manage, and optimize CI/CD pipelines for AI deployments.
• Oversee network and infrastructure connectivity, ensuring seamless communication between distributed systems and GPUs.
• Design and secure tool-calling environments with governance and sandboxing for autonomous actions.
• Provide diagnostic and troubleshooting expertise for AI systems and monitor infrastructure.

Skills

Infrastructure engineering

System integration

Cloud platforms

MLOps / DevOps

Networking concepts

Education

Bachelor’s degree in CS/CE/SE

Tools

Kubeflow

TensorFlow Serving

LangGraph

Kubernetes

Docker

Terraform

Pulumi

Redis

Postgres

Job description

Booz Allen Hamilton is seeking an AI Integration Engineer to design, deploy, and maintain the AI infrastructure that supports LLMs and distributed AI workloads. You will bridge advanced AI models with compute infrastructure and operational workflows to ensure stable deployments.

Ideal candidates have a strong background in high-performance computing, cloud infrastructure, MLOps or DevOps, and AI ecosystem integration.