Top AI Jobs in Chicago, IL

Build and operate AI agents, automations, and evaluation frameworks for Sales, Account Executives, and Customer Success workflows. Connect Salesforce, Outreach, Gong, and signal sources; redesign seller processes; monitor agent performance; and establish human oversight. Serve as product owner and AI advisor for the revenue organization, translating business needs into technical solutions, driving adoption, maintaining the roadmap, and reporting impact on conversion, pipeline, deal cycles, and renewals.

Define architecture for computer vision, VLM, and VLA systems; train, fine-tune, evaluate, optimize, and deploy models on cloud GPUs and edge hardware. Build labeling, active-learning, synthetic-data, and monitoring pipelines, while managing latency, memory, cost, and production reliability. Lead technical projects, make research-to-product decisions, review designs and code, mentor engineers, and collaborate across engineering, research, and product teams.

Develop and maintain generative AI applications using LLMs, RAG, prompt engineering, and orchestration frameworks. Build scalable Python APIs and microservices, collaborate with engineering and product teams, and support deployment, monitoring, troubleshooting, and optimization in Azure and AWS environments. Contribute to CI/CD, automated testing, containerization, infrastructure automation, code reviews, documentation, and Agile delivery.

Leads end-to-end engineering design and delivery for enterprise AI, intelligent automation, document intelligence, and workflow platforms. Defines technology stacks, drives architecture, CI/CD, automation, testing, resiliency, observability, and production readiness. Provides technical thought leadership across engineering and architecture teams, coordinates stakeholders and dependencies, evaluates emerging technologies, and guides complex modernization and cloud adoption initiatives without direct people-management responsibility.

Deploys and supports enterprise AI assistants such as Microsoft Copilot and Claude, performs identity, security, data, licensing, and governance readiness activities, and manages AI platform services. Builds AI agents, automations, prototypes, and lightweight applications using APIs, low-code tools, and AI-assisted development platforms. Leads client workshops, training, adoption, troubleshooting, and responsible AI initiatives while translating business needs into secure, scalable enterprise solutions.

Leads enterprise AI adoption by evaluating vendors, models, platforms, and emerging technologies; designing reusable agentic frameworks and reference architectures; developing prototypes and proofs of concept; and enabling scalable, secure AI solutions. Partners with business, engineering, security, legal, compliance, and procurement teams to define adoption standards, evaluate tradeoffs, and guide AI investments across cloud, distributed systems, APIs, microservices, and DevOps environments.

Leads the global Data Science, Analytics, and AI strategy for Mars Snacking. Responsibilities include building high-performing technical teams, developing AI governance and capabilities, delivering analytics products, partnering with senior stakeholders, guiding technology architecture, managing vendors, advancing AI literacy, and evaluating emerging models and trends to create business value.

Leads enterprise AI strategy, roadmaps, and project portfolios from ideation through deployment. Manages AI scientists and engineers, partners with executives and stakeholders, translates business needs into technical requirements, and drives AI adoption, governance, education, and measurable business impact. Oversees solutions using analytics, machine learning, generative AI, data engineering, and related technologies while establishing success metrics and ensuring timely, budget-conscious delivery.

As a Senior Solutions Architect, you will assist customers in building solutions with NVIDIA's AI technology, focusing on Generative AI and Large Language Models while collaborating across teams for performance analysis.

Define and drive CrowdStrike’s company-wide AI and agentic architecture. Design production AI systems, shared platforms, lifecycle workflows, and developer tooling across products and business units. Improve performance, resilience, scalability, observability, governance, security, and auditability across cloud and edge environments. Build reference implementations, guide architectural decisions, resolve complex engineering challenges, mentor technical leaders, and align senior stakeholders around AI strategy and implementation.

Lead development and production deployment of machine learning systems for time-series anomaly detection in connected-device environments. Build streaming and MLOps pipelines, tune alerts, monitor model drift, and integrate AI systems with LLM-based assistants. Guide architecture, tooling, observability, and production readiness while mentoring and managing applied AI and full-stack engineers. Collaborate across engineering, product, data, and research teams.

Leads data science efforts for foundational AI and automation across a global payments network. Partners with data scientists, engineers, and product managers to develop scalable machine learning engines, AI agents, merchant intelligence, and risk solutions. Serves as an NLP and LLM domain expert, fine-tuning and deploying models using PyTorch, AWS, vector databases, and modern AI technologies. Translates complex ML work into business value while mentoring teams and delivering production-scale platforms and solutions.

Lead a product team building ML and GenAI infrastructure and model delivery for marketing and acquisitions. Define problem statements, set 3-year roadmaps tied to business outcomes, partner with data scientists and engineers, drive reusable scalable capabilities, influence cross-functional stakeholders, recruit and mentor talent, and measure success with data-driven metrics.

Owns the enterprise AI engineering foundation, including shared AI platforms, semantic routing, model hosting, gateways, operations, agentic pipelines, and multi-agent orchestration. The role establishes architecture and standards, delivers secure production AI services, develops evaluation and governance capabilities, manages AI economics, prototypes emerging technologies, mentors senior engineers, and partners with security, risk, compliance, and data governance teams in a regulated financial and healthcare benefits environment.

Develop data-driven collections strategies and advanced models for Venmo BNPL and other PayPal products. Use SQL, Python, AI, experimentation, and analytics to create scalable portfolio measurement, A/B testing, and performance solutions. Partner with Credit Strategy, Fraud, Risk, Operations, Product, Technology, and Compliance teams; translate insights into business strategies; present recommendations to senior leaders; maintain data quality; and mentor junior data scientists.

Set technical direction for Root’s AI platform, including infrastructure, tooling, shared services, governance, reliability, security, observability, and cost management. Lead cross-team initiatives, guide AI and LLM architecture decisions, establish operating standards, shape technical roadmaps, resolve complex tradeoffs, and mentor engineers and technical leaders across the organization.

Develops, deploys, monitors, and optimizes machine learning and Generative AI models. Responsibilities include data preprocessing, feature engineering, exploratory analysis, algorithm refinement, production implementation, cross-functional integration, MLOps, model interpretability, and ethical AI compliance. The role requires experience with cloud platforms, Python, deep learning, LLMs, TensorFlow, PyTorch, and production AI systems.

Lead product vision and strategy for an end-to-end AI-powered software factory. Integrate planning, coding, review, and verification across GitLab Duo Agent Platform and product areas. Collaborate with engineering, design, and GTM teams, identify gaps and dependencies, present recommendations to executives, and drive execution to deliver cohesive developer workflows.

Lead development of AI products from discovery through production: define strategy and roadmap, prototype with agentic tools, translate business problems into scoped AI use cases, own requirements and delivery, design evaluation and quality metrics, support solution architecture, lead cross-functional teams, navigate enterprise AI governance, and mentor product managers.

Designs and leads AI Quality Engineering for enterprise AI applications, LLMs, intelligent automation, and agentic systems. Responsibilities include building validation and testing frameworks, prompt and workflow testing, runtime quality controls, observability, telemetry, drift detection, reliability support, and release validation. The role collaborates across engineering, security, governance, clinical, data, and product teams to validate healthcare workflows and responsible AI deployments, while mentoring quality engineers and advancing AI-enabled testing practices.

Designs and deploys AI-powered features and scalable AI/ML services across SaaS products. Responsibilities include translating business problems into AI solutions, integrating large language models, preparing data, evaluating model performance, writing Python, building secure deployment infrastructure, and collaborating with product, data, and cloud operations teams. The role requires production AI/ML experience, scalable data infrastructure expertise, AWS familiarity, and modern DevOps practices.

Leads CDW’s AI advisory consulting portfolio, including strategy, governance, operating models, adoption, prioritization, and roadmaps. Develops consulting methodologies, delivery frameworks, sales enablement, and thought leadership. Advises executives and customers, leads complex presales pursuits, shapes proposals and business cases, oversees delivery quality and risk, evaluates technical feasibility, and mentors consulting and presales teams.

Design and deliver scalable AI and generative AI applications using LLMs, RAG, agentic frameworks, prompt engineering, evaluation, and fine-tuning. Build backend APIs, microservices, cloud-native systems, and frontend integrations using Angular or React. Lead architecture decisions, develop observability and evaluation tooling, ensure software quality and security, collaborate cross-functionally, and mentor engineers.

Lead end-to-end quality engineering and validation for an AI-enabled healthcare operations analytics product. Design scalable automated validation frameworks, data quality and reconciliation, ontology and metric validation, LLM and AI evaluation, test harnesses, regression suites, and observability. Partner across product, engineering, and domain teams, mentor engineers, and drive root-cause resolution to ensure accurate, reliable, and traceable data, metrics, APIs, analytics, and user workflows.

Designs, builds, deploys, and operates production-grade AI applications for Finance using agentic workflows, RAG, APIs, and integrated financial data. Develops evaluation frameworks, monitoring, governance controls, and secure enterprise AI solutions. Partners with Finance and technology leaders on use cases including variance analysis, reconciliations, close acceleration, policy research, and treasury forecasting. Establishes reusable engineering patterns, supports production readiness, mentors engineers, and ensures auditability, privacy, explainability, and regulatory compliance.