[Hiring] AI Architect @Rackspace Technology

Role Description

The AI at Rackspace (AIR) team is an internal enabling team on a mission to bring AI capabilities to every corner of Rackspace engineering. We build reusable AI infrastructure, agentic workflows, and full stack applications that accelerate the business.

• Define and own the architecture strategy for AI platforms and applications across Rackspace
• Design scalable, reusable AI architecture patterns — including agentic systems, multi-agent workflows, RAG pipelines, and orchestration frameworks
• Define non-functional requirements including scalability, latency, cost efficiency, and security for AI systems
• Create and govern architecture standards, conduct design reviews, and ensure consistency across engineering teams
• Lead build vs. buy vs. partner decisions for AI tooling, frameworks, and infrastructure
• Ensure interoperability across teams, platforms, and services — including frontend, backend, AI, and Kubernetes-based infrastructure
• Own the long-term technical vision for the AI engineering function, beyond individual delivery cycles
• Partner with product, data, and platform teams to shape the AIR team's technical roadmap
• Mentor and grow senior and mid-level engineers through architecture reviews, engineering standards, and technical guidance
• Serve as a key technical voice in cross-team architecture and governance discussions
• Champion responsible AI practices and AI-native software development standards across Rackspace

Qualifications

• Architecture Thinking — Demonstrated ability to design complex, distributed systems; define NFRs; and govern architecture at an organizational level
• AI Systems Design — Hands-on experience designing production-grade agentic systems, RAG pipelines, and LLM-integrated applications
• Technical Leadership — Proven track record of setting engineering direction, leading architecture decisions, and enabling cross-functional teams
• Python — Expert-level; includes async patterns, testing, packaging, and production-grade engineering practices
• Cloud Architecture (AWS) — Deep expertise across compute, networking, storage, and managed AI services; ability to design for scale and cost
• LangChain / LangGraph — Production experience building agentic and orchestration-based systems
• AWS Bedrock — Experience selecting and working with foundation models for real enterprise use cases
• Kubernetes — Ability to design and govern production workloads; familiarity with Helm and resource management
• Full Stack Systems Design — Experience designing end-to-end system and platform capabilities across frontend and backend layers

Requirements

• Experience designing internal developer platforms or AI enablement tooling at scale
• Knowledge of prompt engineering, evaluation frameworks, and LLM observability (e.g., LangSmith)
• Familiarity with MLOps — model versioning, monitoring, and drift detection
• Background in platform engineering — GitOps, service mesh, infrastructure as code (Terraform/CDK)
• Experience with multi-cloud or hybrid cloud environments
• Exposure to AI security, governance, and responsible AI frameworks
• Contributions to open source AI or developer tooling projects

Benefits

• Have led engineering efforts end-to-end and can balance speed with quality
• Think about enabling other teams as much as shipping your own features
• Are opinionated about architecture but pragmatic about trade-offs
• Want to help shape what AI-native engineering looks like inside a major cloud company