Top AI & Machine Learning Jobs in Chicago, IL

Leads Caterpillar’s Condition Monitoring Analytics Transformation team and portfolio. Manages 8–12 data scientists while providing hands-on technical guidance on machine learning, AI, predictive maintenance, failure modeling, evidence clustering, persona modeling, and closed-loop learning. Collaborates with commercial, product, engineering, R&D, and MLOps teams to deploy scalable analytics solutions, prioritize resources, drive innovation, and develop team talent.

Develop and deploy generative AI assistants, processing pipelines, tooling, and software using Python or Go. Build FastAPI services, edge-device components, CI/CD pipelines, and AWS infrastructure. Apply LLMs, prompt engineering, and modern agent architectures using LangChain and LangGraph. Collaborate with data scientists to productionize scalable analytics and AI solutions, and assist with Airflow-based machine learning pipelines. The role also requires software architecture, testing, technical writing, and cross-functional collaboration.

Develop data science and analytics solutions for connected Caterpillar equipment. Responsibilities include gathering, mining, and processing large datasets; building data models; applying NLP, text analysis, machine learning, and statistical methods; defining analysis requirements; communicating insights through visualization; and optimizing algorithms. The role requires Python, database access, cloud technology, version control, and strong communication skills.

Develop machine learning, deep learning, statistical, and Generative AI solutions using equipment and telematics data. Build models for anomaly detection, failure prediction, remaining useful life, and risk assessment. Support digital products through reliability monitoring, incident response, automation, connectivity planning, technical analysis, and performance optimization. Partner with engineers and data scientists on AI, analytics, IoT, and digital product initiatives.

Leads an AI Evaluation Engineering team responsible for validating generative AI products, including intelligent agents and digital assistants. Provides technical direction, manages team performance and development, owns product quality, and establishes engineering best practices. The role requires expertise in GenAI architecture, evaluation methodologies, observability, automated testing, cloud and edge environments, reliability, safety, RAG assessment, and human-in-the-loop validation.