Platform and Reliability Engineer, Senior Manager

Booz Allen Hamilton•Usa, DC

About The Position

The Senior Manager, Platform and Reliability Engineer will build and operate the secure, reliable foundation for enterprise agentic AI. The role provides cloud infrastructure, shared runtimes, model access, deployment pipelines, and observability services that enable engineering teams to deliver and operate AI solutions. It automates environments and security controls; monitors availability, latency, throughput, token consumption, and cost; and leads shared-platform incident response and recovery. In collaboration with AI engineering, data engineering, and forward-deployed engineering teams, the role validates operational readiness and maintains deployment standards and runbooks. Its accountability centers on shared-platform reliability, security infrastructure, and production operations. Booz Allen offers health, life, disability, financial, and retirement benefits, paid leave, professional development, tuition assistance, work-life programs, dependent care, and recognition awards. The projected annualized compensation range is $142,900 to $266,000 USD. Salary depends on factors including location, education, skills, experience, contract-specific affordability, and organizational requirements. The posting will close within 90 days of its posting date. The hiring process includes identity verification using biometrics and AI, and candidates are expected to be on camera during interviews and assessments. Using AI or other tools to assist with interview responses is prohibited unless explicitly permitted. Booz Allen describes its culture as people-first and prioritizes collaboration. Remote employees may occasionally be required to work at a Booz Allen or customer facility; hybrid employees are expected to work at such a facility according to leadership expectations and role needs; onsite work is primarily full-time at a customer facility. All qualified applicants receive consideration without regard to protected status.

Requirements

Experience with Infrastructure-as-Code, containers, orchestration, and CI/CD pipelines, including automated security and deployment checks using tools such as Terraform or CloudFormation.
Experience applying site reliability engineering practices, including service objectives, alerting, incident response, capacity planning, and disaster recovery.
Experience implementing centralized logs, metrics, distributed traces, and dashboards to diagnose production failures and performance issues.
Experience operating model gateways or AI workloads and implementing LLMOps practices.
Knowledge of LLM operational requirements, including model gateways, rate limits, retries, timeouts, runtime isolation, and consumption costs.
Ability to automate operational workflows using languages such as Python or Bash and troubleshoot distributed systems.
Ability to document support responsibilities, evaluate readiness evidence, and coordinate technical handoffs across teams.
Bachelor’s degree and 8+ years of experience building and operating production infrastructure in AWS and Databricks, including networking, identity, secrets management, and access controls; alternatively, 9+ years of that experience in lieu of a degree.

Nice To Haves

Experience with FinOps, automated recovery, performance testing, or secure enterprise environments.

Responsibilities

Build and operate cloud infrastructure, shared runtimes, model access, deployment pipelines, and observability services for enterprise agentic AI.
Automate environments and security controls, including security and deployment checks.
Monitor platform availability, latency, throughput, token consumption, and cost.
Lead shared-platform incident response and recovery.
Work with AI engineering, data engineering, and forward-deployed engineering to validate operational readiness.
Maintain deployment standards and runbooks.
Document support responsibilities, evaluate readiness evidence, and coordinate technical handoffs across teams.