Senior AI Architect – Azure & Cloud AI

IBM

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Work type
On-site
Location
Chicago, IL
Posted
6 days ago

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Similar $208k
$159k most similar roles pay here $272k

This listing doesn't post a salary. Most similar roles pay $170,300–$246,150.

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About IBM

IBM is a US-based global technology company providing hybrid cloud, AI, consulting, enterprise software, and IT infrastructure products and services.

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Listed pay typically runs $1,000,000–$1,000,000 across 8 roles with salary data.

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TL;DR · Senior AI Architect – Azure & Cloud AI

We are seeking a senior AI architect to join our growing team in Chicago for a high-visibility enterprise engagement. This client-facing role requires a seasoned professional who will provide technical leadership and define the architectural vision across multiple workstreams using Azure AI services such as OpenAI, Machine Learning, and data platforms. You will develop reference architectures, mentor team members on best practices, and ensure solutions are scalable, secure, and aligned with business objectives. Responsibilities include leading architecture design sessions, translating complex requirements into technical solutions, and implementing end-to-end AI systems while adhering to enterprise security standards. Preferred skills include experience with Azure data services, agentic AI frameworks like LangChain, and knowledge of responsible AI practices.

What you'll do

  • Define and govern end-to-end Azure AI architecture for complex projects.
  • Develop reference architectures and reusable assets to accelerate AI delivery.
  • Translate business requirements into scalable, secure AI solutions on Azure.
  • Lead design sessions with stakeholders to align on technical approach and strategy.
  • Establish standards for model deployment, data integration, and AI observability.

What we're looking for

  • 8+ years experience leading implementation of enterprise-grade AI solutions in cloud and hybrid environments.
  • Design scalable AI platforms using Azure services for secure model deployment, orchestration, monitoring, and governance.
  • Build and operationalize LLM-powered applications with frameworks like LangChain and LangGraph.
  • Define agentic architectures including multi-agent orchestration, tool calling, memory management, and human-in-the-loop controls.
  • Establish best practices for AI industrialization, including CI/CD, model lifecycle management, observability, prompt/version management.
  • Integrate AI systems with enterprise applications, APIs, vector databases, workflow platforms, and operational systems.
  • Provide technical leadership in architecture reviews, platform strategy, vendor selection, and AI governance initiatives.

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