Principal AI Data Architect

Caterpillar Inc.•Peoria, IL

About The Position

When you join Caterpillar, you're joining a global team who cares not just about the work we do – but also about each other. We are the makers, problem solvers, and future world builders who are creating stronger, more sustainable communities. We don't just talk about progress and innovation here – we make it happen, with our customers, where we work and live. Together, we are building a better world, so we can all enjoy living in it. Join the Global Finance AI & Advanced Analytics organization and help shape how Caterpillar builds and deploys AI-powered solutions across Global Finance. As a Principal AI Architect/Builder, you'll serve as a technical leader and trusted advisor on multiple AI solution design use cases - with a focus on agentic systems that can reason, plan and take action. You'll partner with IT, business functions and product teams to ensure we're building AI solutions that are architecturally sound, scalable and aligned with where Global Finance is heading. This role has a direct impact on Caterpillar's Finance AI strategy and the opportunity to drive impactful results.

Requirements

Effective Communications: Strong effective communication and the ability to effectively transmit, receive, and accurately interpret ideas, information, and needs through the application of appropriate communication behaviors.
AI-Driven Entity Resolution: Develop and implement sophisticated strategies for Entity Resolution (ER) by utilizing Large Language Models (LLMs) and Graph Databases (e.g., Neo4J, AWS Neptune, CosmosDB) to accurately map, reconcile, and standardize accounting data across diverse sources.
Advanced RAG Implementation: Architect and deploy production-grade Retrieval-Augmented Generation (RAG) pipelines for complex data interpretation and standardization. This includes managing the underlying Vector Databases and optimizing prompt/context engineering for high accuracy.
Performance Optimization: Understand performance SLAs. Leverage specialized databases such as OLAP solutions (e.g., DuckDB, ClickHouse) for rapid analytics and column stores/caching (e.g., Redis) for low-latency access.
Cloud Infrastructure and Deployment: Engage with IT experts on cloud deployment strategy (AWS/Azure), containerization (Docker) and orchestration (Kubernetes) to ensure robust, scalable, and observable deployments.
Requirements Analysis: Expert knowledge of tools, methods, and techniques of requirement analysis; ability to elicit, analyze and record required business functionality and non-functionality requirements to ensure the success of a system or software development project.
Data Architecture: Expert knowledge of processes, techniques and factors that affect data architecture; ability to design blueprints on how to integrate data resources for business processes and functional support.
Target Architecture: Expert knowledge of target architecture; ability to develop the IT blueprint and roadmap while aligning the architecture and processes with business strategies and objectives.
Experience with Multiple AI Technology Deployment Solutions: Azure/AWS Cloud-Based AI Solutions, Snowflake Cortex, M365 Copilot Studio, On-Prem AI Experience (e.g. Ollama or similar…)
Modern Data Stack and Databases: Entity Resolution: Proven track record of solving complex entity resolution challenges at scale Graph Databases: Hands-on experience with Neo4J, AWS Neptune, or CosmosDB, specifically applied to ER or MDM Data Warehousing: Deep expertise in Snowflake architecture and optimization. Fast Analytics: Experience utilizing OLAP databases (e.g., DuckDB) and in-memory/column stores (e.g., Redis) for performance optimization

Nice To Haves

Deep experience designing and building agentic AI systems, including multi-agent architectures, tool use and orchestration patterns
Experience designing and implementing production AI agent systems using frameworks like LangChain, LangGraph, Semantic Kernel, or similar orchestration tools
Strong understanding of LLM capabilities, limitations and deployment patterns (prompt engineering, RAG, function calling, context engineering)
Experience evaluating and integrating AI/ML infrastructure components (vector databases, embedding models, orchestration layers, observability tools)
Broad software engineering background including API design, authentication/authorization patterns, and enterprise integration
Ability to translate complex technical concepts for non-technical stakeholders and influence architecture decisions across teams.
Track record of staying current with rapidly evolving AI landscape and brining practical recommendations to the table
Experience with MLOps/LLMOps practices, model monitoring, or production AI systems as scale

Responsibilities

Develop detailed architecture deliverables to solve business problems through agentic AI systems and patterns. Ultimately, delivering full-stack prototypes and production solutions
You will act as a technical expert collaborating with a team of dedicated data scientists, IT professionals, software engineers, accountants, and ERP knowledge owners to deliver AI and Advanced Analytics solutions in support of Caterpillar’s enterprise-scale accounting data system.
Design AI application's technical infrastructure, including orchestration layers, tool integrations, data retrieval patterns, and testing approaches.
Evaluate and deploy of emerging AI technologies and frameworks to enhance Global Finance agentic capabilities.
Translate business requirements into AI solution designs and collaborating with cross-functional teams to deliver results.