Top AI Jobs in Chicago, IL

Lead one or more teams to design, build, and run cloud-native, AI- and data-intensive tax platforms. Provide technical and people leadership, guide architecture and implementation, drive agile and DevOps practices, mentor team members, collaborate cross-functionally, and transform complex tax challenges into scalable solutions.

Lead and develop a software engineering team building cloud-native AI agents, assistants, orchestration frameworks, tooling, and observability solutions. Own technical strategy, architecture, delivery, reliability, security, scalability, operational excellence, and production support. Partner with product, data science, architecture, and business teams while managing releases, resources, risks, and multiple initiatives. Coach engineers, improve engineering practices, and ensure responsible AI and governance compliance.

Build and ship production AI systems across the machine learning lifecycle, including model development, evaluation, serving, monitoring, and API development. Contribute to generative AI, document classification, extraction, LLM, and internal platform tooling. Collaborate with engineers, data scientists, and product stakeholders while developing software engineering, cloud infrastructure, CI/CD, and DevOps skills under mentorship.

Lead real-world evidence studies for pharmaceutical clients using complex clinical, molecular, claims, EHR, and registry data. Design observational analyses, derive endpoints, apply survival analysis and causal inference methods, interpret results, and communicate findings to technical and non-technical stakeholders. Develop reusable analytical code and standards while incorporating LLMs and agentic workflows into research processes. Collaborate cross-functionally to improve data quality, products, and RWE methodologies, with a focus on oncology and clinical development.

Lead real-world evidence studies using complex clinical and molecular data, observational study design, survival analysis, causal inference, and machine learning. Collaborate with pharmaceutical partners, derive clinical endpoints, interpret results, develop reusable analytical code, and apply LLM-based tools to accelerate workflows. Communicate findings to technical and non-technical stakeholders through reports, presentations, manuscripts, and conference materials while partnering cross-functionally to improve data quality and analytical practices.

Leads enterprise architecture, design, governance, and adoption of AI, machine learning, generative AI, analytics, RPA, intelligent automation, and data platform solutions. Translates business strategy into scalable, secure, compliant architectures across Azure, AWS, SaaS, and Microsoft technologies. Partners with business, engineering, security, governance, and operations teams; establishes reusable standards and patterns; guides delivery from concept through production; leads architecture reviews, prototyping, performance improvements, compliance, and technical mentoring.

Leads AI/ML initiatives by defining solution roadmaps, developing and deploying machine learning, deep learning, NLP, and generative AI models, and building batch and real-time production pipelines. Partners with product, engineering, business, legal, and compliance teams to identify impactful use cases, operationalize models on AWS, evaluate performance, and communicate results to technical and executive stakeholders.

Leads large-scale AI and machine learning initiatives, defining problem statements, roadmaps, and production solutions. Develops NLP, generative AI, recommendation, personalization, search, and document question-answering models. Builds scalable batch and real-time ML pipelines on AWS, collaborates with product, engineering, business, legal, and compliance teams, and communicates technical results to senior stakeholders. Oversees experimentation, model evaluation, deployment, and team performance.

Summer Analyst in Data & AI will design and build end-to-end data, analytics, and ML/AI solutions using cloud and modern tooling. Responsibilities include developing models and pipelines, running experiments tied to business KPIs, supporting data governance and compliance, creating dashboards, and collaborating in agile teams while receiving mentorship and training.

Lead technical architecture for AI workflow engagements, defining orchestration, data flows, integrations, deployment models, security guardrails, and operational readiness. Review and approve AI Workflow Engineer builds, manage technical risks, present architectures to senior client stakeholders, and coordinate with platform engineering. Mentor engineering teams, contribute to technical standards, and ensure scalable, secure, maintainable solutions across multiple client engagements.

Build AI-powered workflows, agents, integrations, and internal tools that automate People Operations. Partner with People, Engineering, and IT to modernize onboarding and other employee processes, while implementing secure access controls, human oversight, escalation paths, and production-ready architectures. Prototype with users, iterate quickly, and measure improvements in efficiency, employee experience, and operational consistency.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Leads teams designing and deploying scalable AI and machine learning solutions. Responsibilities include data wrangling, software development, data infrastructure and pipeline management, model training, complex analysis, stakeholder collaboration, project execution, and mentoring. The role uses Python, C++, TensorFlow, and Scikit-Learn to transform data into business insights and drive innovation.

Leads end-to-end delivery of AI/ML use cases, including problem framing, engineering design, model development, deployment, monitoring, and production operations. Builds agentic AI and LLM-powered products using prompt engineering, RAG, fine-tuning, and governance controls. Sets engineering quality standards, mentors engineers, partners with business stakeholders, identifies new AI opportunities, and ensures responsible AI, regulatory, privacy, and cross-border requirements are addressed.

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.

Build and productionize AI agents for customer support, voice interactions, and carrier onboarding. Develop primarily backend applications in Python and Java or Go, design databases and APIs, integrate frontend components, troubleshoot production workflows, conduct code reviews, and deploy services with Kubernetes. Collaborate with product, engineering, DevOps, US teams, and customers to deliver supply chain platform capabilities.

Design and deploy production AI systems that transform large-scale geospatial data into actionable intelligence. Build end-to-end ML pipelines, multimodal and reasoning models, experimentation frameworks, synthetic datasets, and scalable inference systems. Collaborate with research, engineering, product, and domain teams while optimizing models for reliability, latency, and cost. Support monitoring, retraining, evaluation, and next-generation Earth AI capabilities.

Lead complex applied-AI technology programs from initiation through delivery and support. Define scope, plans, governance, risks, and dependencies; coordinate engineering, QA, product, and operations; use enterprise AI capabilities responsibly to accelerate planning and reporting; ensure production readiness and continuous improvement.

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.

Lead and grow a data science team responsible for panel quality, representativeness, and measurement methodology. Advance statistical and ML methods, partner cross-functionally to productionize robust, explainable measurement systems, and communicate technical insights to internal and client audiences.

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.

Year-round intern supporting OCC’s AI Research & Engineering team by designing and prototyping agentic AI workflows, integrating models with internal data and APIs, testing accuracy and safety, documenting solutions, and supporting AI governance. The role offers hands-on experience with generative AI, frontier platforms, enterprise integrations, and cross-functional collaboration in a regulated financial environment.

Lead ZAIDYN's AI Engineering function to design, build, and ship an enterprise-grade agentic AI platform. Own architecture, integrations (AWS Bedrock/AgentCore), LLMOps/eval frameworks, observability, governance, and team hiring/operations. Ensure production readiness, cost/latency controls, auditability, and cross-functional delivery with product and QA while representing the platform externally.

The AI Engineering Specialist will oversee project tasks, manage expectations, mentor junior members, and design scalable AI systems while working with advanced AI models and ML pipelines.