Top AI Jobs in Chicago, IL

Lead design and delivery of scalable AI/ML systems: data wrangling, model training, deployment, and infrastructure. Manage teams, mentor staff, own project planning/budgeting, and collaborate with stakeholders to drive AI-driven business insights.

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.

Design and implement AI-enabled integration solutions, cloud architectures, and enterprise technology platforms. Lead strategic planning, analyze business requirements, conduct feasibility studies, coach teams, manage stakeholders, and validate solutions with clients. Apply generative and agentic AI, IoT, sensor data, systems thinking, and enterprise architecture practices to solve complex business problems and drive innovation.

Lead design and deployment of scalable AI and ML systems, drive business development, mentor AI engineering teams, collaborate with clients, implement data infrastructure and pipelines, and apply Python/Java to productionize models.

Lead design, development, and deployment of scalable AI/ML systems. Oversee data wrangling, integration, modeling, and software implementation. Use Python, C++, Java and ML libraries (TensorFlow, Scikit-Learn). Collaborate with stakeholders, validate outcomes, coach diverse teams, and drive innovation to deliver client-focused AI solutions at scale.

Design, build, and deploy AI and machine learning solutions at scale. Develop data pipelines and scalable models, perform data wrangling and analysis, apply NLP and neural networks, collaborate with clients and teams, and contribute to open-source and iterative improvement of AI systems.

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.

Lead client engagements to assess business challenges, define enterprise data and AI strategies, develop roadmaps, guide solution design and delivery, provide technical leadership and mentoring, and support practice growth via presales and thought leadership.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

The Enterprise Architect will design scalable AI solutions, mentor teams, support business development, and manage client relationships while evolving technology practices.

Design and implement AI agents, plugins, automations, and enterprise integrations for HR Technology platforms. Lead requirements gathering, technical analysis, solution design, testing, documentation, deployment, security, and data integrity efforts across HR, IT, and business stakeholders. Integrate platforms including ServiceNow, Workday, and other enterprise applications while supporting the HR automation and AI roadmap.

Build and manage AI-powered workflows, agents, automations, and internal applications for Deal Desk and GTM teams. Identify automation opportunities, develop prototypes, scale solutions to production, integrate enterprise data and APIs, implement human validation and audit controls, optimize LLM costs, document tools, measure adoption, and train users. The role requires Deal Desk or RevOps experience plus practical knowledge of AI models, APIs, scripting, SQL/SOQL, Python, CPQ, CLM, Salesforce, and agentic platforms.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Leads innovation within Enterprise Change Management by identifying AI and automation opportunities, designing future-state processes, prototyping and deploying intelligent solutions, and driving adoption. Oversees innovation frameworks, policies, controls, roadmaps, integrations, and enterprise partnerships. Works across business, technology, risk, and operations teams to deliver measurable productivity, compliance, transparency, and change-management improvements.

Lead an agile engineering team to architect, develop, and productionize secure, scalable software that integrates LLMs and generative AI. Drive AI-assisted engineering practices, mentor engineers, translate business requirements into technical roadmaps, automate reliability, and ensure compliant, high-quality delivery.

Drives AI adoption across a regional Wealth Management field organization. Builds relationships with advisors and leadership, translates AI capabilities into practical workflows, delivers training and coaching, supports product and technology rollouts, identifies business development opportunities, gathers feedback, and develops strategies to sustain adoption across client engagement, prospecting, campaigns, and growth activities.

Own strategic customer relationships and lead deployment of AI workflows into supplier operations. Map customer SOPs and data sources, identify high-ROI AI opportunities, translate needs into product and implementation requirements, drive adoption, partner with product/engineering, build playbooks, track value, and surface expansion opportunities across global teams.

Owns the backlog, sprint cadence, releases, and production health of enterprise AI products. Translates product strategy into requirements, user stories, work packages, and sprint goals while coordinating engineers, data scientists, business partners, and dependent teams. Leads Scrum ceremonies, PI planning, testing, release communications, and uptime monitoring. Applies AI tooling to improve delivery and uses technical fluency to prototype, review pull requests, and support responsible AI implementation.

Lead data science engagements with customers to acquire, clean, analyze, and model data; develop scalable operational models and evaluation methods; coach engineers on best practices; drive solution strategy, ethics, and industry thought leadership; write extensible code and collaborate across teams to deliver cloud-based AI solutions.

Lead hands-on engineering for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Define APIs and abstractions, make architecture and scalability decisions, improve observability and reliability, and lead a small team of senior engineers through technical reviews, mentorship, and production delivery.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Leads enterprise AI-native engineering initiatives by researching and prototyping AI tools, designing AI-enhanced workflows, creating reference architectures, and developing proofs of concept. Owns AI training and enablement, including documentation, presentations, tutorials, demonstrations, and coaching. Evaluates tooling, supports adoption, communicates technical changes to leadership, hosts engineering office hours, and establishes scalable standards across the organization.

The Machine Learning Scientist will develop AI frameworks for predictive modeling in oncology, collaborating with teams to enhance drug R&D efforts through advanced machine learning techniques and multimodal data integration.

Design, build, and deliver production AI/ML solutions using agile practices. Translate business requirements into ML specifications, produce secure high-quality code, create architecture artifacts, analyze large datasets, troubleshoot complex issues, and contribute to engineering communities and continuous improvement.

Build and deploy AI-augmented workflows and agents to automate internal processes across departments. Identify high-value automation opportunities, optimize operational workflows, create reusable documentation and enablement resources, measure business impact, and help establish safe AI governance standards. The role requires cross-functional consulting, stakeholder management, process analysis, and practical experience with low-code platforms, LLM APIs, prompt engineering, or AI agent frameworks.