AI Architect – IN

Rackspace · India, Remote

Hybrid Full-time Senior Operations

Job Description

Title:Software Architect IV

Shift:Remote

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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Requirements

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
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