Cloud Engineer

Booz Allen Hamilton • McLean, VA

10d • $86,800 - $198,000

About The Position

The Cloud Engineer designs, builds, and operates secure cloud platforms and reusable tooling that enable generative AI, machine learning, and AI-enabled applications for mission-critical U.S. government customers. The role combines cloud infrastructure and software engineering with hands-on support for AI platforms and tooling, helping customers move AI use cases from prototype to production across AWS, Azure, or GCP. It involves building cloud foundations, automating deployment workflows, and supporting capabilities such as model access, prompt management, retrieval-augmented generation, agent orchestration, evaluation, and observability.

The engineer serves as a technical point of contact for cloud and AI tooling, troubleshoots technical issues, and balances proactive platform engineering with responsive customer support. The role identifies root causes across distributed systems and improves reliability, security, performance, and cost efficiency. It also builds monitoring and evaluation capabilities for infrastructure health, model and agent behavior, latency, token consumption, retrieval quality, and service failures, and creates and maintains technical documentation, reference architectures, runbooks, and knowledge base articles.

The position requires independent engineering and close collaboration with application developers, data scientists, security teams, platform teams, and customers. The engineer handles urgent operational issues while advancing platform enhancements and automation. U.S. citizenship is required due to the nature of work performed within the facility.

Requirements

• 3+ years of experience developing and operating cloud solutions using Python and services in AWS, Azure, or GCP.
• 2+ years of experience in customer-facing technical support, platform engineering, or solutions engineering.
• 1+ years 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 due to the nature of work performed within the facility.

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 supporting 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 resolve technical issues in a timely manner.
• Identify root causes across distributed systems and improve the reliability, security, performance, and cost efficiency of AI-enabled services.
• Handle urgent operational issues while maintaining progress on platform enhancements and automation.

Benefits

• Health, life, disability, financial, and retirement benefits.
• Paid leave.
• Professional development.
• Tuition assistance.
• Work-life programs.
• Dependent care.
• Recognition awards program for exceptional performance and demonstration of company values.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level