Top AI & Machine Learning Jobs in Chicago, IL

Build, test, and optimize Google Cloud-native applications and generative AI solutions across the software development lifecycle. Translate client needs into technical designs, develop AI-enabled features using Vertex AI, LLM APIs, prompt engineering, RAG, Python, LangChain, and vector databases, and evaluate model performance and guardrails. Collaborate with stakeholders, review engineering deliverables, manage workstreams, and mentor junior team members.

Designs and delivers conversational and agentic AI solutions for contact centers, including virtual agents, intelligent routing, agent assist, IVR modernization, and workflow automation. Translates client needs into requirements, user flows, prototypes, and interaction designs; conducts discovery, user research, usability testing, and workflow analysis; collaborates with analytics, development, and product teams; reviews platform configurations; analyzes feedback and behavioral data; mentors junior staff; and supports client relationships and delivery workstreams.

Develop AI and data analytics solutions for AML and sanctions clients within PwC’s Risk & Regulatory practice. Responsibilities include Python-based data analysis, designing data architectures and pipelines, creating dashboards, interpreting complex datasets, engaging stakeholders, and delivering actionable insights and tailored client solutions. The role also involves client relationship building, problem-solving, continuous improvement, and contributing to strategic business outcomes.

Leads AI engineering initiatives for tax technology, designing and deploying machine learning solutions using Python, deep learning, and NLP. Builds data pipelines, validates models, guides experimentation, and translates tax business needs into scalable production systems. Oversees complex workstreams, directs cross-functional teams, presents technical recommendations, coaches team members, and ensures solutions meet technical, client-service, policy, and responsible AI standards.

Leads multidisciplinary teams delivering AI-enabled, cloud-native applications on AWS. Oversees architecture, development, testing, deployment, modernization, platform engineering, security, reliability, and client advisory activities. Translates business objectives into technical roadmaps, guides complex engineering work, coordinates cross-functional architecture reviews, communicates delivery risks and options, and coaches teams across multiple workstreams.

Designs, builds, tests, and troubleshoots full-stack cloud-native applications on AWS. Translates client needs into technical solutions across architecture, development, testing, and delivery; builds scalable infrastructure and integration patterns; applies AI-enabled engineering methods; and supports modernization initiatives. The role also reviews technical work, mentors junior engineers, collaborates with architecture teams, and helps deliver cloud, data, and analytics solutions.

Leads AWS-native application design, development, testing, modernization, and architecture efforts. Manages technical workstreams, delivery timelines, scope, and team allocation while coaching engineers on cloud infrastructure, AI-native engineering, testing, and troubleshooting. Reviews technical designs and code, coordinates application and infrastructure activities, supports client architecture discussions, and improves delivery reliability and speed.

Analyzes complex data sets, develops Python-based data solutions and pipelines, and applies advanced analytics to support client strategy and business performance. Designs dashboards and visualizations, collaborates with cross-functional teams, manages stakeholders, improves data quality, and builds client relationships. The role also involves solving complex business challenges, identifying industry trends, and delivering high-quality work within PwC’s Risk & Regulatory practice.

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 and coach teams to design, develop, and deliver production-grade AI and data solutions for clients. Manage client accounts, ensure quality and timelines, analyze complex datasets, implement best practices, and drive continuous improvement while fostering client relationships and technical excellence.