Cloud Engineer
Booz Allen · McLean, Virginia, United States
Cloud EngineerThe Opportunity:
Would you like to engineer secure, scalable cloud platforms that enable teams to build and operate artificial intelligence solutions for mission-critical customers? Do you have a passion for cloud automation, developer experience, and the reliable operation of generative AI and machine learning tooling? This is an opportunity to apply modern cloud and AI technologies to help customers supporting U.S. government agencies move AI use cases from prototype to production while solving complex technical problems and delivering responsive, professional support.
The team is seeking a highly skilled and motivated cloud engineer who combines strong infrastructure and software development capabilities with hands-on experience in AI platforms and tooling. You should be able to build secure cloud foundations, automate deployment workflows, and support tools for model access, prompt management, retrieval-augmented generation, agent orchestration, evaluation, and observability. You will work across AWS, Azure, or GCP services and should understand Linux and Windows operating systems, networking, identity and access management, containers, infrastructure as code, CI/CD, and the operational characteristics of AI workloads.
As a member of the team, you will serve as a technical point of contact for cloud and AI tooling, conduct troubleshooting sessions, and provide timely resolution of technical issues. You will balance proactive platform engineering with reactive customer support, identify root causes across distributed systems, and improve the reliability, security, performance, and cost efficiency of AI-enabled services. You will build monitoring and evaluation capabilities for infrastructure health, model and agent behavior, latency, token consumption, retrieval quality, and service failures, addressing anomalies before they become major customer problems. You will also create and maintain technical documentation, reference architectures, runbooks, and knowledge base articles.
This role requires independent engineering, close collaboration with application developers, data scientists, security teams, and customers, and flexibility in a fast-paced environment where reliability and customer satisfaction are priorities. You will handle urgent operational issues while maintaining progress on platform enhancements and automation. Due to the nature of work performed within this facility, U.S. citizenship is required.
What You’ll Work On:
- 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 to streamline 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 that support testing, evaluation, release management, rollback, and traceability for 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.
Join us. The world can’t wait.
You Have:
- 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
Nice If You Have:
- 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
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 mor