Cloud Engineer

Booz Allen Hamilton•McLean, VA

11d•$86,800 - $198,000•Hybrid

About The Position

Booz Allen is seeking a cloud engineer to build, operate, and support secure, scalable cloud platforms and reusable tooling for generative AI, machine learning, and AI-enabled applications serving mission-critical customers. The role combines infrastructure and software development with hands-on support for AI platforms and tooling across AWS, Azure, or GCP. The engineer will help move AI use cases from prototype to production by automating deployment workflows and supporting capabilities such as model access, prompt management, retrieval-augmented generation, agent orchestration, evaluation, and observability. The position balances proactive platform engineering with customer-facing technical support. The engineer will act as a technical point of contact, troubleshoot distributed systems, resolve issues promptly, and improve service reliability, security, performance, and cost efficiency. The work includes monitoring infrastructure and AI behavior, identifying anomalies, and maintaining technical documentation, reference architectures, runbooks, and knowledge base articles. The role involves independent engineering and close collaboration with application developers, data scientists, security teams, platform teams, and customers in a fast-paced environment. U.S. citizenship is required because of the nature of work performed within the facility.

Requirements

At least 3 years of experience developing and operating cloud solutions using Python and services in AWS, Azure, or GCP.
At least 2 years of experience in customer-facing technical support, platform engineering, or solutions engineering.
At least 1 year of experience implementing or supporting AI, machine learning, or generative AI solutions in a cloud environment.
Experience with managed AI platforms and services such as Amazon Bedrock, Amazon SageMaker AI, Microsoft Foundry, Azure Machine Learning, Azure OpenAI, or Vertex AI.
Experience implementing infrastructure as code and CI/CD using tools such as Terraform, AWS CloudFormation, AWS CDK, Bicep, GitHub Actions, GitLab CI, Azure DevOps, or Jenkins.
Experience with containers and orchestration technologies such as Docker, Kubernetes, Amazon EKS, Azure Kubernetes Service, or Google Kubernetes Engine.
Experience with cloud networking, identity and access management, secrets management, encryption, logging, monitoring, and secure software delivery practices.
Knowledge of multi-account, multi-subscription, or multi-tenant cloud architectures and methods for isolating workloads and data.
Ability to explain complex cloud and AI concepts to technical and non-technical stakeholders, manage multiple support cases, and remain calm and professional under pressure.
Bachelor's degree in Computer Science, Engineering, or Information Systems.
U.S. citizenship is required.

Nice To Haves

Experience designing production retrieval-augmented generation systems, including document ingestion, chunking, embeddings, vector search, reranking, grounding, and evaluation.
Experience building or operating AI agents, tool integrations, workflow orchestration, or Model Context Protocol–based solutions.
Experience with LLMOps, MLOps, or GenAIOps tools for experiment tracking, prompt and model versioning, automated evaluation, deployment, drift detection, and quality monitoring.
Experience implementing responsible AI practices, including risk assessment, human oversight, content safety, privacy protection, red teaming, and testing for harmful or unreliable outputs.
Experience supporting workloads in regulated, disconnected, classified, or restricted cloud environments.
Experience with API management, event-driven architecture, serverless computing, data pipelines, and vector databases.
Experience with cloud FinOps and AI cost optimization, including token usage analysis, model routing, caching, autoscaling, and capacity planning.
Experience creating and delivering technical training, workshops, demos, or onboarding for developers and end users.
Knowledge of Agile or Scrum practices, ITIL, or similar service management frameworks, and tools including Confluence, Jira, and Microsoft Office.
ITIL Foundation, AWS Certified Generative AI Developer - Professional, AWS AI Practitioner, Microsoft Certified: Azure AI Engineer Associate, Microsoft Certified: Machine Learning Operations Engineer Associate, Google Cloud Professional Machine Learning Engineer, or a Cloud Security or Networking Specialty Certification.

Responsibilities

Design, build, and operate secure cloud platforms and reusable tooling for generative AI, machine learning, and AI-enabled applications.
Implement automation and infrastructure as code for provisioning, configuration, model endpoint deployment, and environment promotion.
Develop and maintain services and integrations for foundation model access, prompt and configuration management, retrieval-augmented generation, vector search, agent tools, and API gateways.
Create CI/CD, MLOps, and GenAIOps workflows for testing, evaluation, release management, rollback, and traceability of models, prompts, agents, and infrastructure.
Implement security controls for sensitive data and AI workloads, including least-privilege access, secrets management, encryption, network isolation, audit logging, content safeguards, and policy enforcement.
Build observability for infrastructure, applications, model endpoints, and agents, including metrics for availability, latency, errors, token usage, cost, retrieval quality, and output quality.
Evaluate and recommend cloud and AI services, models, frameworks, and tools based on mission fit, security, performance, reliability, interoperability, and cost.
Optimize AI workloads for scalability, resiliency, inference performance, and cloud cost efficiency.
Collaborate with customers, developers, data scientists, security teams, and platform teams to translate requirements into supported technical solutions.
Create and maintain reference architectures, technical documentation, runbooks, knowledge base articles, and onboarding materials.
Serve as a technical point of contact for cloud and AI tooling, conduct troubleshooting sessions, and provide timely resolution of technical issues.
Identify root causes across distributed systems and address anomalies before they become major customer problems.