AI​/ML Solution Architect

Overview

Career Area: Technology, Digital and Data

Job Description:

Your Work Shapes the World at Caterpillar Inc.

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.

We’re looking for an AI/ML Solution Architect to lead the design of AI-enabled applications and platforms, with a strong emphasis on generative AI (LLMs, RAG, agents) and modern solution architecture. This role blends hands-on technical expertise with strategic design leadership to deliver secure, scalable, and intelligent enterprise solutions. This position is ideal for someone who is passionate about emerging AI capabilities and can translate them into practical, production-ready architecture.

• Design and architect AI powered and GenAI enabled applications and services.
• Define solution patterns for LLMs, SLMs, embeddings, vector search, RAG pipelines, and AI agents.
• Guide teams on selecting and integrating AI components and cloud native services.
• Establish secure, scalable, and responsible AI architecture standards.
• Embed ML/GenAI features into enterprise applications and operational systems.
• Support MLOps/LLMOps workflows including deployment, monitoring, governance, and prompt management.
• Partner with Engineering, Product, Platform, and Cybersecurity teams to deliver high quality solutions.
• Influence AI strategy and help drive enterprise AI adoption.
• Architect end-to-end data flows supporting AI/ML workloads, ensuring high quality data ingestion, transformation, storage, and retrieval across structured, semi-structured, and unstructured data sources.
• Ensure AI system designs align with enterprise data models, data governance policies, data retention standards, and lineage requirements.

• 10+ years of experience in enterprise technology, with strong background in solution or software architecture.
• Hands-on experience integrating machine learning and/or generative AI into real-world applications.
• Familiarity with LLM frameworks and tools (e.g., Lang Chain, Hugging Face, Semantic Kernel, vector databases).
• Knowledge of cloud native architecture, microservices, and distributed systems.
• Understanding of RAG workflows, embeddings, model integration, and AI safety patterns.
• Experience with MLOps/LLMOps, model lifecycle management, and production AI systems.
• Ability to simplify complex technical concepts and influence technical and business stakeholders.
• Strong foundational knowledge of enterprise data architecture, including data modeling (conceptual, logical, physical), metadata management, master data management (MDM), and data integration patterns.
• Experience designing and optimizing data platforms such as data warehouses, data lakes, lake houses, and real-time data pipelines.
• Working knowledge of various data formats (tabular, time series, geospatial, image, text, sensor/IoT, transactional) and how they are leveraged in AI/ML workloads.
• Ability to evaluate dataset quality, data drift, feature quality, and bias to support robust model development.
• Solid grasp of applied mathematics and statistics (e.g., probability, distributions, linear algebra, optimization basics, error metrics) sufficient to collaborate effectively with data scientists and understand model behavior.
• Familiarity with feature engineering concepts, feature stores, and data preprocessing methodologies.

• AI/ML & Generative AI solution design
• LLM orchestration, prompt engineering, RAG
• Cloud native architecture (AWS/Azure/GCP)
• Microservices, APIs, distributed systems
• Secure architecture & responsible AI principles
• Strong communication & leadership
• Data architecture & data engineering fundamentals
• Understanding of data modeling, ETL/ELT, and pipeline orchestration tools
• Applied statistical reasoning and ability to interpret ML model outputs alongside data scientists

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