Senior AI Architect – Azure & Cloud AI

IBM Consulting offers a career built on long-term client relationships and global collaboration. You’ll work with leading companies across industries, helping shape their hybrid cloud and AI journeys. With support from strategic partners, IBM technology, and Red Hat, you’ll drive meaningful change and accelerate client impact. IBM Consulting fosters a culture of curiosity, growth, and empathy, valuing your unique skills while supporting your long-term career development.

About the Role

We are seeking a senior, client-facing AI Architect to join our growing AI practice on a high-visibility enterprise engagement. In this role, you will provide thought leadership and define the technical direction for 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 serve as the go-to technical authority for end-to-end Azure AI architecture, ensuring solutions are scalable, secure, governed, and aligned with business value.

Responsibilities

• 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 to 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, embedding enterprise standards and governance from the start
  • Present architectural recommendations with clarity 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
  • Standardize deployment, monitoring, governance, and support processes 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
  • Implement orchestration and guardrail frameworks for agentic workflows to ensure collaboration, contextual access, and oversight
  • Ensure adherence to enterprise security, compliance, and reliability requirements
  • Collaborate with data, engineering, and business teams for end-to-end delivery

Requirements

• 8+ years of experience leading 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, 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, and operational systems
• Collaborate with business, data, security, and engineering teams to translate requirements into scalable AI architectures
• Provide technical leadership across architecture reviews, platform strategy, vendor selection, and AI governance initiatives

Qualifications

Preferred: Master’s Degree

Skills

• Familiarity with agentic AI frameworks (e.g., LangChain, AutoGen, Semantic Kernel) and RAG architectures
• Experience with Azure data services (Azure Synapse, Azure Data Factory, or Microsoft Fabric)
• Knowledge of AI governance, responsible AI practices, and enterprise security considerations
• Azure certifications (e.g., Azure Solutions Architect Expert, Azure AI Engineer Associate)