Rackspace Technology
AI Architect
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About Rackspace Technology™
Rackspace Technology™ is a leading end-to-end, hybrid cloud and AI solutions company. We can design, build, and operate our customers' cloud environments across all major technology platforms, irrespective of technology stack or deployment model. We partner with our customers at every stage of their cloud journey, enabling them to modernize applications, build new products, and adopt innovative technologies.
Named a best place to work by Newsweek and Forbes, we attract and develop world-class talent to deliver a Fanatical Experience™ – so our customers can achieve better outcomes and stay ahead of what’s next.
The AI at Rackspace (AIR) Team
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.
What You'll Do
- 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
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Must-Have Skills
- 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
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Good-to-Have Skills
- 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
You'll Thrive Here If You
- 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
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