Enterprise Solution Architect

Enterprise AI Solution ArchitectA 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 ResponsibilitiesEnterprise AI Solution ArchitectureArchitect end-to-end enterprise AI solutions that integrate AI models, data platforms, and enterprise systems into cohesive, production-grade architecturesDefine integration patterns across AI services, APIs, data pipelines, and core business systems to ensure interoperability and extensibilityTranslate complex business requirements into scalable AI-powered solution designs, with clear articulation of tradeoffs, risks, and value driversLead architecture design reviews and provide hands-on guidance to delivery teams across active engagementsContribute to practice development by building reusable assets, frameworks, and point-of-views that strengthen our AI delivery capabilityPlatform Engineering & Cloud InfrastructureDesign and implement Azure-based AI platform infrastructure that supports scalable model deployment, data ingestion, and application integrationDefine infrastructure-as-code standards and CI/CD pipeline patterns for AI workloads, enabling repeatable and auditable deploymentsArchitect cloud-native environments optimized for AI throughput, cost efficiency, and operational resilienceEstablish platform engineering standards that enable development teams to build, test, and ship AI solutions faster and more reliablyEvaluate and recommend emerging platform capabilities, cloud services, and tooling that advance our delivery approachObservability, Governance & Development LifecycleDesign observability frameworks for AI systems including model monitoring, prompt evaluation, drift detection, and performance alertingDefine AI governance standards covering responsible AI principles, audit trails, explainability requirements, and human-in-the-loop controlsEstablish development lifecycle practices for AI — from experimentation and model validation through to production deployment and ongoing operationsImplement guardrail frameworks and safety controls for agentic and generative AI workloads to ensure appropriate oversight and auditabilityChampion MLOps and AI engineering best practices across the team, ensuring solutions are maintainable, observable, and continuously improvableThought Leadership & Practice GrowthServe as a senior technical voice both internally and with clients, sharing expertise, identifying emerging patterns, and helping define our AI point of viewMentor architects and engineers on platform engineering, governance, and AI architecture best practicesEngage in pre-sales and solutioning activities, helping shape proposals and articulate our technical differentiationThis 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 integrationHands-on experience designing enterprise AI solutions on Azure, including Azure OpenAI, Azure Machine Learning, and supporting data servicesStrong grounding in platform engineering, infrastructure-as-code (e.g., Terraform, Bicep), and CI/CD pipeline design for AI workloadsExperience building observability and monitoring solutions for AI/ML systems in production environmentsProven ability to define and enforce AI governance, responsible AI, and compliance frameworks at enterprise scaleComfortable leading design conversations with both technical teams and client stakeholdersPreferred technical and professional experience:Familiarity with agentic AI architectures, orchestration frameworks (LangChain, Semantic Kernel, AutoGen), and RAG pipeline designExperience with Azure data platform services including Microsoft Fabric, Azure Synapse Analytics, and Azure Data FactoryKnowledge of MLOps practices including model versioning, A/B testing, canary deployments, and feedback loop designBackground contributing to a consulting practice or Center of Excellence — building frameworks, POVs, and reusable assetsAzure certifications such as Azure Solutions Architect Expert, Azure AI Engineer Associate, or DevOps Engineer ExpertIBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.