AI Cloud Platform Site Reliability EngineerThe Opportunity:

Mission users are increasingly relying on agentic AI systems to support complex workflows, accelerate analysis, and improve decision advantage. Unlike traditional software systems, agentic AI platforms introduce operational complexity across model invocations, workflow orchestration, tool integrations, retrieval and knowledge layers, safety controls, and probabilistic outputs. As an AI Platform Site Reliability Engineer (SRE), you’ll help ensure the availability, resiliency, observability, and operational integrity of an AWS GovCloud-based agentic AI platform supporting national defense missions.

In this role, you’ll serve as the reliability owner for production AI operations. You’ll work cross-functionally with multiple stakeholders, including with cloud engineering, platform engineering, AI agent development, MLOps, data science, and customer knowledge teams to operationalize their work in production through monitoring, alerting, Service Level Indicators (SLI) and Service Level Objectives (SLO) management, incident response, ticket triage, change control, and automation. You won’t be duplicating model development, data science, or cloud platform build responsibilities. Instead, you’ll ensure that the system, its agents, and their supporting services remain healthy, traceable, performant, and supportable in mission environments.

Define, implement, and maintain service level indicators, service level objectives, error budgets, dashboards, alarms, and escalation paths for an agentic AI platform operating in AWS GovCloud.

Monitor end-to-end health and performance of agent workflows, model invocations, retrieval or knowledge integrations, orchestration steps, tool calls, and dependent services.

Triage incidents, alerts, and operational tickets. Lead root-cause analysis, coordinate recovery actions, and drive post-incident corrective actions that reduce mean time to recovery and prevent recurrence.

Build and maintain observability pipelines across metrics, logs, traces, audit telemetry, and operational events using AWS-native tooling and approved enterprise observability tooling.

Establish and tune operational thresholds for latency, availability, error rates, token and cost consumption, workflow success rates, tool failure rates, guardrail interventions, and drift-related signals.

Partner with platform engineers, cloud engineers, AI agent developers, MLOps engineers, data scientists, and customer SMEs to define ownership boundaries, handoffs, rollback criteria, release readiness gates, and operational support models.

Coordinate with MLOps and data science teams when model or data quality degradation, drift, or unexpected behavior requires rollback, retraining, prompt changes, knowledge-base updates, or other corrective actions.

Automate remediation and routine operational tasks using Python, shell scripting, infrastructure as code, and event-driven workflows to reduce manual toil.

Support secure and compliant operations in regulated national defense environments, including auditability, least-privilege access, controlled logging, and disciplined change management.

Work with limited direction, mentor junior team members, and help mature AI operations practices across the program.