AI Integration Engineer

Booz Allen Hamilton; Maryland; On-site; USD 112,800 - 257,000; Full time

Benefits offered by this job

Comprehensive benefits package

Professional development opportunities

Tuition assistance

Job summary

Booz Allen Hamilton seeks an AI Integration Engineer in Maryland to design and maintain the infrastructure for AI systems. Your expertise in high-performance computing and cloud services like AWS and Azure will be critical for managing AI workloads and deploying applications efficiently.

The role demands over 5 years of infrastructure experience, strong knowledge of MLOps, and familiarization with various AI/ML frameworks. A TS/SCI clearance is required for this position, ensuring compliance with security protocols.

We offer a competitive compensation range of $112,800 to $257,000 yearly.

Qualifications

• 5+ years of experience in infrastructure engineering or system integration.
• 2+ years supporting large-scale AI/ML systems.
• Experience with cloud platforms and AI services.
• Familiar with industry-standard networking and security protocols.
• Experience with NVIDIA GPU technologies.

Responsibilities

• Integrate LLMs and AI workloads across systems.
• Maintain scalable GPU computing infrastructure.
• Develop CI/CD pipelines for AI deployments.
• Ensure seamless communication between systems.
• Provide troubleshooting expertise for AI systems.

Skills

Infrastructure engineering

System integration

Cloud platforms (AWS, Azure, Google Cloud)

Networking concepts (TCP/IP, DNS, NGINX)

GPU technologies

MLOps (MLflow, Kubeflow)

Orchestration frameworks

AI/ML frameworks (PyTorch, TensorFlow)

Education

Bachelor's degree in CS, Computer Engineering, or Systems Engineering

Tools

Kubernetes

Docker

Terraform

Job description

AI Integration Engineer

Your growth matters to us – explore our career development opportunities.

We are seeking a highly motivated AI Integration Engineer to design, deploy, and maintain the infrastructure that supports artificial intelligence systems, including large language models and distributed AI workloads. This role bridges advanced AI models, compute infrastructure, and operational workflows. You will manage AI readiness by architecting scalable infrastructure, integrating complex systems, and maintaining operational excellence to ensure stable deployments of AI and machine learning applications. The ideal candidate has strong experience in high‑performance computing, cloud infrastructure, MLOps or DevOps, and AI ecosystem integration.

What You’ll Work On

• Serve as the technical point of contact for integrating LLMs and other AI workloads across infrastructure systems, operational tools, and application pipelines.
• Architect, deploy, and maintain scalable GPU computing environments and infrastructure required for autonomous agentic workflows, including persistent state management, long‑term memory systems such as vector databases, and multi‑step reasoning tasks.
• Develop, manage, and optimize CI/CD pipelines for AI deployments, ensuring smooth transitions from model development to production environments.
• Oversee network and infrastructure connectivity, ensuring seamless communication between distributed systems, GPUs, virtual machines (VMs), APIs, and Command and Control (C2) tools.
• Design and secure tool‑calling environments where agents interact with external APIs, ensuring strict governance and sandboxing for autonomous actions.
• Provide diagnostic and troubleshooting expertise for AI systems, monitoring infrastructure to maintain availability, security, and performance benchmarks.
• Collaborate across engineering, data, and AI teams to align infrastructure solutions with business and operational goals.

You Have

• 5+ years of experience in infrastructure engineering or system integration roles.
• 2+ years of experience supporting large‑scale AI/ML systems or GPU‑centric environments.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud, and their AI‑focused services, including SageMaker, GCP AI Platform, and Azure Machine Learning.
• Experience with networking concepts, including TCP/IP, DNS, NGINX, load balancing, and firewalls, applied to AI model and infrastructure deployment.
• Experience integrating MLOps pipelines using tools such as MLflow, Kubeflow, TensorFlow Serving, or Vertex AI, including integration of AgentOps frameworks such as LangSmith and Arize Phoenix, to monitor autonomous decision‑making paths and agent reasoning tasks.
• Experience with orchestration frameworks for multi‑agent systems such as LangGraph, CrewAI, or AutoGen, and managing the stateful databases required to support them, including Redis and Postgres.
• Experience working with NVIDIA GPU technologies, including CUDA, NCCL, TensorRT, and DGX systems, and container or orchestration tools such as Kubernetes, Docker, Terraform, or Pulumi.
• Ability to manage and optimize distributed, high‑performance computing environments, including clusters of GPUs and cloud‑based GPU instances.
• TS/SCI clearance with a polygraph.
• Bachelor's degree in CS, Computer Engineering, or Systems Engineering.

Nice If You Have

• Experience with AI/ML frameworks for model training and deployment such as PyTorch, TensorFlow, or Hugging Face Transformers.
• Experience implementing observability and monitoring systems such as Grafana, Prometheus, and ELK for AI infrastructure to track performance and operational health.
• Experience with security practices for AI systems, including encryption, role‑based access controls, secure APIs, and compliance frameworks such as SOC 2 and GDPR.
• Experience with agentic safety, including the implementation of Human‑in‑the‑Loop approval gateways and automated kill switches for autonomous processes.
• Experience with vector database infrastructure such as Pinecone, Weaviate, or Milvus, and Retrieval‑Augmented Generation (RAG) pipelines used to provide agents with contextual memory.
• Knowledge of distributed computing frameworks such as Ray, Horovod, or Dask for AI training jobs.
• Knowledge of AI ethics and operational risk assessments, ensuring deployed systems align with organizational policies and standards.
• Certified Kubernetes Administrator (CKA) or Kubernetes Application Developer (CKAD) certification.
• AWS Certified Solutions Architect or similar cloud certification.
• NVIDIA certifications such as the NVIDIA Certified Advanced GPU Infrastructure Specialty Certification.

Clearance

Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information; TS/SCI clearance with polygraph is required.

Compensation

At Booz Allen, we celebrate your contributions and provide a comprehensive benefits package, including health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work‑life programs, and dependent care. Full‑time and part‑time employees working at least 20 hours a week are eligible for Booz Allen’s benefit programs. The projected compensation range for this position is $112,800.00 to $257,000.00 (annualized USD). This posting will close within 90 days from the posting date.

Commitment to Non‑Discrimination

All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.