Top AI & Machine Learning Jobs in Chicago, IL

Leads data science and model risk oversight for credit card underwriting models. Builds, evaluates, validates, monitors, and implements machine learning models using large-scale structured and textual data. Partners with data scientists, engineers, and product managers to translate complex statistical findings into business outcomes, ensure effective model governance, and identify emerging technologies and analytical methods.

Leads data scientists, business analysts, and data analysts in developing machine learning models from design through implementation. Partners with software engineering and product teams to deliver customer-focused products, using Python, cloud computing, Spark, H2O, and other technologies to analyze large-scale numeric and textual data. Translates complex technical findings into business goals while developing team talent and evaluating emerging methods.

Leads data science for foundational AI and automation across a global payments network. Partners with data scientists, engineers, ML engineers, and product managers to develop scalable models and AI agents using PyTorch, AWS, vector databases, and LLMs. Serves as an NLP and LLM domain expert, translates technical work into business value, develops talent, and delivers production-scale libraries, platforms, and solutions.

Leads data science initiatives using machine learning, statistical modeling, and large-scale data analysis. Partners with software engineers and product managers to develop customer-focused products, builds and validates models across the full development lifecycle, translates technical findings into business outcomes, evaluates emerging technologies, and develops data science talent. Requires substantial experience with programming, machine learning, relational databases, cloud computing, and people management.

Leads data science and machine learning initiatives for payment-network applications, including fraud, risk, merchant intelligence, and monitoring. Partners with data scientists, engineers, and product managers to build scalable models and AI tools. Responsibilities span model design, training, evaluation, validation, and implementation using Python, AWS, H2O, Spark, and other technologies. The role also requires stakeholder communication, innovation, and team talent development.

Build, deploy, scale, and operate production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing models and data pipelines, managing cloud-based distributed systems, automating testing and deployment, monitoring production models, and applying responsible AI practices. The role collaborates with Product and Data Science teams in an Agile environment and uses technologies such as Python, PyTorch, Spark, Kubernetes, and cloud platforms.

Build, deploy, scale, and maintain production machine learning models and platforms. Responsibilities include designing ML systems, developing data pipelines, operating distributed systems and cloud infrastructure, automating testing and deployment, monitoring models, applying responsible AI practices, and collaborating with product, data science, and engineering teams. The role requires advanced programming, machine learning, distributed computing, Kubernetes, and cloud experience.

Architect, build, and deploy enterprise-scale generative AI and autonomous agent systems for HR technology. Lead multi-agent workflows, full-stack cloud applications, distributed microservices, and technical initiatives while ensuring security, governance, compliance, and operational excellence. Partner with product managers, architects, and risk teams; establish engineering standards; mentor developers; evaluate emerging technologies; and reduce technical debt. The role requires expertise in programming, databases, APIs, cloud platforms, CI/CD, infrastructure as code, RAG, vector search, and LangChain or LangGraph.

Build, deploy, scale, monitor, and maintain production machine learning models and infrastructure. The role involves developing ML components, optimizing algorithms and data pipelines, operating distributed systems and cloud-based platforms, automating testing and deployment, and applying responsible AI and model governance practices. Candidates collaborate with product, data science, and Agile engineering teams while using technologies such as Python, PyTorch, Spark, Kubernetes, and major cloud platforms.

Lead a data science team developing scalable AI and NLP solutions for customer-facing financial applications. Partner with data scientists, engineers, product managers, and designers to build, train, evaluate, fine-tune, and operationalize machine learning and large language models. Drive research into generative AI, training optimization, self-supervised learning, explainability, and RLHF while delivering production systems at scale. Translate technical findings into business outcomes and develop team talent.