Top AI Jobs in Chicago, IL

Develop product analytics and data science solutions for small-business insurance and banking products. Apply statistical analysis, predictive modeling, experimentation, AI, and customer journey analytics to improve product performance, engagement, lead generation, and client outcomes. Establish KPI and measurement frameworks, analyze data using Python and SQL, communicate insights to senior stakeholders, and identify scalable AI and agentic use cases in a regulated environment.

Develop, deploy, and monitor fraud detection models and pattern-recognition pipelines across lending products. Analyze large transactional datasets using Python and SQL, partner with Fraud Operations to refine models, reduce false positives and negatives, improve underwriting and verification, apply AI in production, and lead and mentor team members.

Build and operate enterprise AI platforms, semantic routing, model gateways, agentic workflows, evaluation harnesses, and production AI services. Develop secure, compliant integrations for regulated financial services, optimize AI workload economics, and establish observability, governance, and reliability standards. Prototype emerging capabilities, create reusable architecture components, mentor engineers, lead technical learning, and partner with security, risk, compliance, and data governance teams.

Develop and deploy generative AI assistants, agents, tooling, and software using Python and cloud technologies. Build CI/CD pipelines, deploy AWS services with CloudFormation, and use frameworks including LangChain, LangGraph, Semantic Kernel, and CrewAI. Develop FastAPI services, collaborate with data scientists to productionize analytics solutions, and support machine learning pipelines with Apache Airflow. Serve as a technical lead on complex projects and contribute to software architecture, testing, scalability, and technical documentation.

Build and deploy enterprise AI/ML solutions, including generative AI, agentic systems, RAG pipelines, LLM-powered chatbots, and model-serving microservices. Develop model training, evaluation, monitoring, and production integration workflows while collaborating with data science, engineering, MLOps, and product teams. Prototype emerging AI techniques, improve scalability and performance, and support critical IT escalations. The role requires strong Python and machine learning expertise, with preferred experience in Azure, Databricks, Kafka, Kubernetes, and GPU-based model deployment.

Serve as the CEO's operational right hand, managing executive calendar, travel, communications, contracts, and cross-functional project execution. Maintain dashboards, SOPs, and documentation, coordinate leadership communication and recruiting, remove blockers, recommend process improvements, and represent the company at meetings and events to increase operational efficiency and CEO focus on strategy and customers.

Lead enterprise AI architecture across products, platforms, and shared capabilities. Define architecture standards, reusable patterns, governance, and guardrails for LLM applications, agentic workflows, orchestration, evaluation, observability, security, and compliance. Partner with business, product, and engineering leaders, guide technology adoption and platform modernization, influence senior stakeholders, participate in architecture reviews, and mentor technical leads and engineers.

Builds, validates, and continuously improves AI-enabled pharmacy solutions as the embedded pharmacist domain expert. Translates pharmacist workflows and clinical reasoning into agent logic, guardrails, evaluation rubrics, quality standards, and acceptance criteria. Partners with product, engineering, design, quality, legal, and editorial teams to assess AI accuracy, safety, traceability, and workflow fit. Owns domain quality through launch and ongoing monitoring, researches relevant AI regulations, and represents the company in pharmacy AI thought leadership.

Drives customer adoption of BrazeAI by leading workshops, diagnostics, and 2–6 week accelerators. Identifies and prioritizes AI use cases, guides customers from ideation through agent launches, resolves technical and organizational barriers, documents best practices, and advises on ROI. The role builds trusted relationships with technical and marketing stakeholders, provides AI thought leadership, collaborates cross-functionally, and supports the AMER region from New York with travel up to 25%.

Build and operate production AI agents and automated workflows for business teams. Develop trusted data foundations on Databricks, integrate SaaS and internal systems through APIs and orchestration tools, and implement evaluation, monitoring, cost, latency, and failure instrumentation. Partner with Data Engineering, Data Science, Core Engineering, and business stakeholders to deliver reusable agent components, APIs, documentation, and production workflows handling regulated consumer data.

Build agentic AI products that enable customers to create custom detections and workflows from operational camera data. Own projects from customer discovery through design, implementation, production operation, and ongoing optimization. Develop backend services, APIs, and customer-facing experiences; partner with platform, machine learning, product, and design teams. Improve AI accuracy, reliability, performance, and cost while mentoring engineers and shaping technical architecture.

Lead Samsara’s AI initiatives across the software development lifecycle, including developer agents and SRE AI. Set strategy, select experiments, make build-versus-buy decisions, control infrastructure and token costs, scale automated incident response, and build trust in AI-driven actions. Manage and grow a team of five to ten senior engineers while partnering directly with R&D leaders and developers.

Build and own production Vision AI systems end to end, including model selection, inference pipelines, evaluation, monitoring, and customer-facing features. Shape architecture, collaborate with product, design, and engineering teams, and use AI coding tools to accelerate development. Establish high standards for quality, testing, reliability, security, and privacy while mentoring engineers and taking ambiguous ideas from prototype to production.

Architect and build Toast’s Vision AI platform, including video ingestion, processing, storage, inference pipelines, evaluation, and monitoring. Develop production ML services and customer-facing computer vision features, make architecture decisions, write significant code, and guide technical direction. Partner across teams, mentor engineers, establish reliability, security, and privacy standards, and promote AI-assisted development. The role requires deep distributed-systems and cloud expertise, production ML experience, video familiarity, and the ability to take ambiguous ideas from prototype to customer-ready products.

Build and deploy production AI applications for acquired companies within the M&A group. Responsibilities include discovering business use cases, prototyping, developing tested code, integrating AI platforms with enterprise systems, creating agentic workflows, managing cloud infrastructure and operations, monitoring production solutions, training users, measuring business impact, and communicating progress to stakeholders. The role also contributes to standards, process improvements, and guidance for junior colleagues.

Apply established machine learning techniques—including clustering, classification, and anomaly detection—to ICS/OT cybersecurity data. Integrate model outputs into batch and near-real-time pipelines, evaluate third-party models, maintain reliable data contracts and observability, and troubleshoot production issues such as data drift and output quality. Collaborate with AI engineers, data engineers, and product teams while clearly communicating model behavior, uncertainty, and downstream use.

Designs, develops, integrates, tests, and deploys AI/ML applications and MLOps solutions for clients. Builds model-serving workflows, APIs, RAG solutions, enterprise integrations, and containerized NVIDIA AI services. Troubleshoots software, model, data, API, and platform issues; creates technical documentation and client handoff materials; contributes to project estimates and implementation plans. This client-facing role also manages scope, schedule, budget, deliverables, and occasional travel to client sites.

Leads the full lifecycle of client technology engagements, from technical discovery and solution design through delivery. Architects AI-enabled software solutions, develops proposals and estimates, shapes commercial and staffing plans, and serves as a technical lead after sale. Builds and coaches delivery teams, manages technical risk, and guides clients on practical AI adoption, including LLMs, agentic systems, evaluation, security, and responsible AI. The role also contributes to thought leadership and may perform hands-on development.

Conduct applied AI research and develop automation, knowledge management, semantic search, retrieval, and reasoning systems. Build and evaluate proof-of-concepts, create frameworks for assessing AI risks and business impact, and collaborate with engineering teams to productionize successful concepts. The role explores agentic, causal, explainable, neuro-symbolic, and knowledge-enhanced AI, while contributing to publications and patents across financial services, fraud, risk, insurance, and marketing applications.

Develop and lead enterprise AI engineering solutions, including Python tooling, RAG systems, LLM agent workflows, vector database integrations, and AI platform connectors. Administer AWS Bedrock, evaluate models using MMLU and TruthfulQA, establish prompt engineering and model versioning standards, and provide technical leadership for scalable AI data systems. Collaborate with product and domain teams to deploy AI-enabled products, define roadmaps, and monitor system effectiveness.

Build full-stack Vision AI products for restaurant operators, including backend APIs, video pipelines, AI model integrations, monitoring, and customer-facing interfaces. Own platform components, contribute to architecture, collaborate across product and engineering teams, and use AI-assisted development tools. The role requires production software experience, cloud infrastructure expertise, event-driven systems knowledge, and comfort working across backend, frontend, data pipelines, and machine learning integrations.

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.

Leads enterprise AI applications and operational effectiveness across contact center operations. Owns AI use cases, ROI, automation roadmaps, virtual agent improvement, workforce optimization, technology deployment, vendor partnerships, and continuous improvement. Builds business cases, monitors performance metrics, drives change adoption, and leads distributed teams while collaborating with IT, Analytics, HR, Quality, and Product stakeholders.

Lead end-to-end performance of customer-facing conversational AI and knowledge base. Design, test, and optimize AI conversations, define intents and success metrics, analyze KPIs, maintain enterprise knowledge, run continuous improvement using analytics and experimentation, and partner cross-functionally to improve CX and business outcomes.

Leads strategy, roadmap, investment, and execution for horizontal AI capabilities, shared AI platforms, and AI-native cybersecurity products. Translates AI research into reliable, secure, scalable customer solutions; drives adoption across product teams; establishes launch and autonomy criteria; partners with customers and cross-functional leaders on commercialization; and builds a high-performing AI product organization.