AI Integration Engineer

The Opportunity:

We are seeking a highly motivatedAI Integration Engineerto join our team and help design, deploy, and maintain the infrastructure that supports artificial intelligence (AI) systems, including Large Language Models (LLMs) and distributed AI workloads. This role is critical to bridging the gap between advanced AI models, compute infrastructure, and operational workflows. You will be responsible for managing AI readiness by architecting scalable infrastructure solutions, integrating complex systems, and maintaining operational excellence to ensure stable deployments of AI and machine learning applications. The ideal candidate has a strong background in high-performance computing, cloud infrastructure, MLOps or DevOps, and AI ecosystem integration.

This is an exciting opportunity to be at the forefront of AI operational infrastructure and contribute to cutting-edge projects.

What You'll Work On:

• Serve as thetechnical point of contactfor integrating LLMs and other AI workloads across infrastructure systems, operational tools, and application pipelines.
• Architect, deploy, and maintain scalableGPU computing environmentsand infrastructure required for autonomous agentic workflows, including persistent state management, long-term memory systems such as Vector DBs, and multi-step reasoning traces.
• Develop, manage, and optimize CI/CD pipelines forAI deployments, ensuring smooth transitions from model development to production environments.
• Overseenetwork 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.
• Providediagnostic and troubleshooting expertisefor 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.

Join us. The world can't wait.

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 withcloud platformssuch as AWS, Azure, or Google Cloud, and their AI-focused services, including SageMaker, GCP AI Platform, and Azure Machine Learning
• Experience with networking concepts, includingTCP/IP, DNS, NGINX, load balancing, and firewalls, applied to AI model and infrastructure deployment
• Experience integratingMLOps pipelinesusing 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 traces
• 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 withNVIDIA 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 implementingobservability and monitoring systemssuch as Grafana, Prometheus, and ELK, for AI infrastructure to track performance and operational health
• Experience withsecurity practicesfor 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 (HITL) 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 ofdistributed computing frameworkssuch as Ray, Horovod, or Dask, for AI training jobs
• Knowledge ofAI ethics and operational risk assessments, ensuring deployed systems align with organizational policies and standards
• Certified Kubernetes Administrator (CKA)orKubernetes Application Developer (CKAD) Certification
• AWS Certified Solutions Architector similar Cloud Certifications
• NVIDIA Certifications such as theNVIDIA Certified Advanced GPU Infrastructure Specialist 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, provide you with opportunities and choices, and support your total well-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work-life programs, and dependent care. Our recognition awards program acknowledges employees for exceptional performance and superior demonstration of our values. Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen's benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits. We encourage you to learn more about our total benefits by visiting the Resource page on our Careers site and reviewing Our Employee Benefits page.

Identity Statement

As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.

Candidate AI Usage Policy

AI is a part of our daily work at Booz Allen, and we are committed to the responsible and ethical use of AI tools. However, we want to ensure a fair candidate process based on your own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in-person or virtual) is prohibited unless permission is explicitly provided.

Work Model
Our people-first culture prioritizes the benefits of collaboration, whether it occurs in person or virtually. To support engagement and effective communication, employees working virtually are generally expected to have their cameras on during meetings.

• Remote: If this position is listed as remote, there may still be occasions when you are required to work in person at a Booz Allen or customer facility.

• Hybrid: If this position is listed as hybrid, you will be expected to work from a Booz Allen facility frequently, in alignment with leadership expectations and the needs of the role. You may also be required to work from or visit a customer facility.

• Onsite: If this position is listed as onsite, work will primarily be performed at a Booz Allen office or customer facility, where employees will collaborate directly with colleagues and customers as required by the role.

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.