AI Architect - IN

Requirements

• Title: Software Architect IV Shift: Remote About the 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
• 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 /
• 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 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)