Enterprise AI Solution Architecture

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

Enterprise AI Solution Architecture

• Architect end-to-end enterprise AI solutions that integrate AI models, data platforms, and enterprise systems into cohesive, production-grade architectures
• Define integration patterns across AI services, APIs, data pipelines, and core business systems to ensure interoperability and extensibility
• Translate complex business requirements into scalable AI-powered solution designs, with clear articulation of tradeoffs, risks, and value drivers
• Lead architecture design reviews and provide hands-on guidance to delivery teams across active engagements
• Contribute to practice development by building reusable assets, frameworks, and point-of-views that strengthen our AI delivery capability
  
  

Platform Engineering & Cloud Infrastructure

• Design and implement Azure-based AI platform infrastructure that supports scalable model deployment, data ingestion, and application integration
• Define infrastructure-as-code standards and CI/CD pipeline patterns for AI workloads, enabling repeatable and auditable deployments
• Architect cloud-native environments optimized for AI throughput, cost efficiency, and operational resilience
• Establish platform engineering standards that enable development teams to build, test, and ship AI solutions faster and more reliably
• Evaluate and recommend emerging platform capabilities, cloud services, and tooling that advance our delivery approach
  
  

Observability, Governance & Development Lifecycle

• Design observability frameworks for AI systems including model monitoring, prompt evaluation, drift detection, and performance alerting
• Define AI governance standards covering responsible AI principles, audit trails, explainability requirements, and human-in-the-loop controls
• Establish development lifecycle practices for AI — from experimentation and model validation through to production deployment and ongoing operations
• Implement guardrail frameworks and safety controls for agentic and generative AI workloads to ensure appropriate oversight and auditability
• Champion MLOps and AI engineering best practices across the team, ensuring solutions are maintainable, observable, and continuously improvable
  
  

Thought Leadership & Practice Growth

• Serve as a senior technical voice both internally and with clients, sharing expertise, identifying emerging patterns, and helping define our AI point of view
• Mentor architects and engineers on platform engineering, governance, and AI architecture best practices
• Engage in pre-sales and solutioning activities, helping shape proposals and articulate our technical differentiation
  
  

This role can be performed from anywhere in the US.

Required Technical And Professional Expertise

• 8 years of experience in enterprise architecture, with demonstrated expertise spanning AI/ML, cloud infrastructure, and systems integration
• Hands-on experience designing enterprise AI solutions on Azure, including Azure OpenAI, Azure Machine Learning, and supporting data services
• Strong grounding in platform engineering, infrastructure-as-code (e.g., Terraform, Bicep), and CI/CD pipeline design for AI workloads
• Experience building observability and monitoring solutions for AI/ML systems in production environments
• Proven ability to define and enforce AI governance, responsible AI, and compliance frameworks at enterprise scale
• Comfortable leading design conversations with both technical teams and client stakeholders
  
  

Preferred Technical And Professional Experience

• Familiarity with agentic AI architectures, orchestration frameworks (LangChain, Semantic Kernel, AutoGen), and RAG pipeline design
• Experience with Azure data platform services including Microsoft Fabric, Azure Synapse Analytics, and Azure Data Factory
• Knowledge of MLOps practices including model versioning, A/B testing, canary deployments, and feedback loop design
• Background contributing to a consulting practice or Center of Excellence — building frameworks, POVs, and reusable assets
• Azure certifications such as Azure Solutions Architect Expert, Azure AI Engineer Associate, or DevOps Engineer Expert