Top AI & Machine Learning Jobs in Chicago, IL

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Leads pricing and revenue data science initiatives for consumer markets clients. Develops predictive models, forecasts trends and customer behavior, manages dashboards and visualizations, ensures data quality, and translates complex analytics into strategic decisions. Oversees client engagements, validates outcomes, directs complex projects, and coaches diverse, high-performing teams.

Leads the development and implementation of conversational and agentic AI solutions for contact centers and other user interaction platforms. Manages multidisciplinary teams, translates client requirements into technical specifications, oversees UI development and human-centered design, conducts usability testing, and builds client relationships. Coaches junior staff, identifies innovation opportunities, and ensures successful project delivery aligned with client strategies and quality standards.

Leads AI modeling and data analytics strategies for AML and sanctions clients in banking and capital markets. Oversees multiple projects, develops strategic solutions, supports business development, maintains executive client relationships, and mentors teams. Responsibilities include improving data quality and architecture, validating analytics integrity and compliance, promoting AI-human collaboration, driving thought leadership, and converting market opportunities into business outcomes.

Leads AI and data-driven solutions for AML and sanctions clients in banking and capital markets. Responsibilities include developing AI solutions with Python, managing data architecture and pipelines, overseeing analytics and visualization deployments, guiding advanced analytics teams, engaging stakeholders, validating client outcomes, improving processes, and coaching diverse teams through complex situations.

Leads AI technology integration and deployment within the tax practice. Oversees data infrastructure, pipelines, modeling, quality validation, and multi-source integration. Develops Python-based analytics solutions using machine learning and natural language processing, conducts rapid experimentation, and drives data-informed innovation. Coaches and develops high-performing teams while collaborating with stakeholders and advising on strategic initiatives.

Leads AI engineering and advanced analytics initiatives for AML and sanctions clients. Develops scalable data, visualization, and analytics solutions; validates data integrity, security, and compliance; identifies system linkages and optimization opportunities; manages client relationships and project execution; and mentors teams delivering innovative AI solutions.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Lead design and delivery of advanced data and ML solutions, performing exploratory analysis, statistical modeling, and visualization to inform strategy. Manage and coach teams, collaborate with clients to validate outcomes, and improve analytics processes and data quality to drive business decisions.

Lead and oversee multiple multidisciplinary teams to architect, develop, deploy, monitor, and maintain full-stack AI solutions (including LLM integrations). Provide strategic and technical leadership, drive experimentation and CI/CD practices, ensure quality and stakeholder alignment, promote innovation, and mentor senior and junior team members to deliver business-aligned AI outcomes.