Senior AI Technical Architect

Position: Senior AI Technical Architect

Location: Westchester, IL (Hybrid)
Engagement: 12 months, extendable
Start Date: Early October 2026
Experience: 15+ years in enterprise technology, with significant experience in AI/ML, GenAI, and enterprise architecture

THE OPPORTUNITY

We are seeking a Senior AI Technical Architect to lead the architecture, governance, and delivery of enterprise-scale AI and GenAI solutions. This is a senior client-facing role responsible for transforming a large portfolio of AI opportunities into a governed, value-driven, production-ready AI roadmap.

The architect will build upon Ingredion's existing AI initiatives, including Ask Ingredion, manufacturing AI/ML capabilities, Microsoft technologies, SAP, and enterprise analytics platforms. The role will partner closely with business, technology, enterprise architecture, security, and executive stakeholders to identify high-value AI opportunities, establish technical and governance standards, and guide distributed engineering teams through implementation.

The ideal candidate combines deep technical expertise in AI/ML and GenAI with strong enterprise architecture, governance, stakeholder management, and delivery leadership capabilities.

KEY RESPONSIBILITIES

1. AI Strategy & Portfolio Leadership

• Assess and prioritize a portfolio of approximately 150 AI opportunities based on business value, technical feasibility, risk, complexity, cost, and time-to-value.
• Lead rapid 4 6-week AI discovery and assessment engagements for multiple high-priority use cases.
• Translate business opportunities into a structured, funded AI delivery roadmap.
• Establish a scalable framework for identifying, evaluating, prioritizing, and industrializing AI use cases.
• Partner with executive sponsors to communicate portfolio progress, investment requirements, risks, and business outcomes.

1. Enterprise AI Architecture

• Define end-to-end enterprise AI architecture covering application, data, integration, AI/ML, security, infrastructure, and observability layers.
• Design scalable AI solutions using an Azure-centric technology ecosystem, integrating with Microsoft 365 Copilot, Teams, Azure AI services, SAP, Power BI, and enterprise APIs.
• Architect solutions leveraging LLMs, RAG, agentic AI, AI orchestration, intelligent automation, and enterprise integration patterns.
• Establish reusable architecture patterns for Assist, Act, and Decide agent models.
• Evaluate emerging AI technologies and platforms while maintaining a strong focus on enterprise standards, interoperability, security, and total cost of ownership.
• Minimize unnecessary introduction of new technology and identify capability gaps where new platforms or tooling provide measurable value.

1. Technical Leadership & Delivery

• Provide technical direction and architectural oversight to distributed engineering teams and offshore delivery pods.
• Establish technical standards, coding and architecture guidelines, reusable components, reference implementations, and engineering best practices.
• Conduct architecture reviews, design reviews, technical assessments, and production-readiness reviews.
• Guide teams through complex technical challenges and remove architectural and engineering blockers.
• Ensure solutions meet enterprise requirements for scalability, reliability, security, performance, maintainability, and operational readiness.
• Establish a repeatable AI delivery "factory" model that accelerates the transition from validated use cases to production.

1. AI/GenAI Technical Expertise

• Provide hands-on architectural leadership across:
• Large Language Models (LLMs)
• Generative AI
• Agentic AI
• Retrieval-Augmented Generation (RAG)
• AI agents and multi-agent architectures
• AI orchestration
• Prompt engineering
• Model evaluation
• Vector databases and semantic search
• Enterprise API integration
• AI application security
• Observability and tracing
• Demonstrate strong experience with Azure OpenAI, Azure AI Foundry, Microsoft Copilot ecosystem, or comparable enterprise AI platforms.
• Define patterns for integrating AI capabilities with enterprise systems such as SAP, Microsoft 365, Teams, Power BI, and other business applications.

1. Stakeholder & Executive Management

• Act as the senior technical advisor and trusted AI architecture partner to business and technology leadership.
• Facilitate workshops with stakeholders across manufacturing, supply chain, finance, quality, operations, IT, and corporate functions.
• Translate complex AI and technology concepts into clear business outcomes and executive-level recommendations.
• Manage competing priorities and align business, technology, security, architecture, and delivery teams.
• Present architecture decisions, investment recommendations, risks, dependencies, and progress to senior leadership.
• Build strong relationships with executive sponsors, product owners, enterprise architects, engineering leaders, and external partners.

1. Value Realization & Adoption

• Define measurable success criteria for AI initiatives, including adoption, productivity, quality, cost savings, cycle-time reduction, and business ROI.
• Establish mechanisms to track AI solution performance and business outcomes after production deployment.
• Partner with business owners to drive adoption and organizational change.
• Ensure AI solutions deliver measurable business value rather than remaining technology prototypes.

WHAT YOU BRING

Required Experience

• 15+ years of overall experience in enterprise technology, software engineering, solution architecture, or technical consulting.
• 5+ years of experience in AI/ML, Generative AI, intelligent automation, or AI-enabled enterprise solutions, with progressively increasing technical leadership responsibilities.
• Proven experience as a Technical Architect, Enterprise Architect, Solution Architect, AI Architect, or equivalent senior technology leadership role.
• Strong experience designing and delivering complex, enterprise-scale technology solutions.
• Demonstrated experience establishing AI architecture, governance, security, and operational standards.
• Strong hands-on understanding of LLMs, RAG, Agentic AI, AI orchestration, evaluation frameworks, and enterprise AI integration.
• Experience with Azure OpenAI, Azure AI Foundry, Microsoft AI ecosystem, or comparable enterprise AI platforms.
• Strong understanding of enterprise integration patterns, APIs, cloud architecture, data platforms, identity, security, and observability.
• Experience leading distributed/offshore engineering teams across multiple time zones.
• Proven experience working directly with senior business, technology, security, and executive stakeholders.
• Strong ability to balance business value, technical feasibility, risk, cost, scalability, and time-to-market.