Senior AI Architect – Azure & Cloud AI

Introduction A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

Your Role And Responsibilities About the Role

We are seeking an AI Architect to join our growing AI practice on a high-visibility enterprise engagement. This is a senior, client-facing role where you will provide thought leadership and define the technical direction across a complex, multi-workstream AI program. The client expects a seasoned professional who can hit the ground running — shaping strategy, establishing architecture standards, and driving alignment between business objectives and AI capabilities.

You will be the go-to technical authority for end-to-end Azure AI architecture, ensuring that every solution is scalable, secure, governed, and aligned to real business value.

What You’ll Do

Thought Leadership & Technical Direction

• Serve as the senior technical voice on the engagement, setting the architectural vision and ensuring consistency across workstreams
• Define and govern end-to-end Azure AI architecture spanning Azure OpenAI, Azure Machine Learning, and enterprise data platforms
• Develop reference architectures, patterns, and reusable assets that accelerate delivery and elevate the practice
• Mentor and upskill team members on Azure AI best practices, design patterns, and emerging capabilities

Client Engagement & Solution Architecture

• Lead architecture design sessions and workshops with client stakeholders to align on strategy and technical approach
• Translate complex business requirements into scalable, secure, and maintainable AI-powered solutions
• Map AI use cases to measurable business value, ensuring enterprise standards and governance are embedded from the start
• Present architectural recommendations with clarity and confidence to both technical and executive audiences

Technical Delivery

• Architect and oversee implementation of AI solutions across Azure OpenAI, Azure ML, and supporting data infrastructure
• Establish design standards for model deployment, prompt engineering, data integration, and AI observability
• Establish a scalable operating model for AI industrialization by standardizing deployment, monitoring, governance, and support processes across existing AI solutions to improve reliability, reuse, and speed to value
• Leverage Model Context Protocols (MCPs) to enable secure, modular integration between agents, enterprise systems, and data sources, creating a more interoperable and extensible agentic AI ecosystem
• Implement orchestration and guardrail frameworks for agentic workflows to ensure AI agents can collaborate effectively, access the right contextual information, and operate with appropriate oversight, auditability, and human-in-the-loop controls
• Ensure all solutions adhere to enterprise security, compliance, and reliability requirements
• Collaborate closely with data, engineering, and business teams to ensure successful end-to-end delivery

Preferred Education Master’s Degree

Required Technical And Professional Expertise

• 8+ years of experience leading the implementation of enterprise-grade AI and agentic AI solutions across cloud and hybrid environments.
• Design scalable AI platforms leveraging Azure and AWS services, including secure model deployment, orchestration, monitoring, and governance.
• Build and operationalize LLM-powered applications using frameworks such as LangChain and LangGraph.
• Define agentic architectures including multi-agent orchestration, tool calling, memory management, MCP integration patterns, and human-in-the-loop controls.
• Establish best practices for AI industrialization, including CI/CD, model lifecycle management, observability, prompt/version management, evaluation frameworks, and guardrails.
• Integrate AI systems with enterprise applications, APIs, vector databases, workflow platforms, a…