Platform Engineer

• Design, develop, and operate scalable backend services, APIs, and distributed platform capabilities for enterprise AI applications, models, and agents.
• Build and integrate AI platform capabilities including model access and routing, AI gateways, agent runtimes, lifecycle management, tool integration, model serving, retrieval services, and related enterprise AI services.
• Develop secure identity, authentication and authorization, secrets management, tool access, permissions, and enterprise-system integration capabilities.
• Automate deployment across development, test, and production environments using cloud-native technologies, containers, Kubernetes, CI/CD, and infrastructure-as-code.
• Build observability capabilities including logging, metrics, tracing, monitoring, alerting, execution telemetry, and cost visibility.
• Develop capabilities for AI evaluation, model lifecycle management, MLOps, configuration, versioning, and production operations.
• Design services for scalability, availability, resilience, performance, security, and support across commercial cloud, hybrid, on-premises, and restricted environments.
• Partner with AI Architecture, Applied AI, Application Engineering, Cybersecurity, Data, and product teams to translate needs into reusable enterprise platform capabilities.
• Continuously improve the developer experience and accelerate AI adoption across RTX.

AI Platform Engineer

• Define, implement, and maintain key performance metrics, SLOs, and SLIs
• Align reliability metrics with business objectives and operational goals
• Manage error budgets and collaborate with development teams to balance reliability and feature delivery
• Analyze incidents and outages to inform error-budget adjustments
• Design and implement comprehensive monitoring solutions, dashboards, and alerts using Prometheus, Grafana, Loki, Tempo, and other observability platforms
• Architect, design, and implement scalable cloud infrastructure for multiple business applications
• Develop and implement automated quality gates for release reliability and performance standards
• Lead the release DevOps team in integrating quality gates into CI/CD pipelines
• Assist with incident response using metrics and monitoring insights
• Conduct post-mortem analyses, identify root causes, and recommend preventive measures
• Use AI to identify changes, anomalies, and next-best actions from metrics, logs, and traces
• Apply GenAI to accelerate incident triage and root-cause analysis
• Monitor AI workloads for quality, safety, cost, latency, and reliability with end-to-end tracing and secure logging/redaction
• Embed AI signal checks into CI/CD to identify SLO risk, latency/error drift, and regression patterns before production release
• Work closely with cross-functional teams to improve reliability, performance, and scalable growth of cloud-based systems

Platform Engineer jobs in Arizona

AI Platform Engineer

Staff Observability Platform Engineer – SRE