Top AI Engineer Jobs in Chicago, IL

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.

Build and ship governed, production-grade AI applications using Claude and other foundational models. Develop reusable skills, plug-ins, MCP connections, RAG workflows, embeddings, evaluations, monitoring, and enterprise integrations. Productionize prototypes, manage AI asset governance, enforce security and access controls, and provide production support. Collaborate with business, engineering, security, cloud, and data teams in a regulated environment.

Build and govern enterprise AI applications using Claude, retrieval-augmented generation, plug-ins, skills, and agentic workflows. Establish engineering standards, architecture, testing, evaluation, monitoring, security, and governance practices. Productionize prototypes, manage AI asset catalogs, optimize cost, latency, and reliability, and provide technical leadership through code reviews, mentoring, and cross-functional collaboration. The role also supports production incidents and partners with engineering, security, cloud, data, and business teams.

Build and operate enterprise AI platforms across Azure and GCP, including Kubernetes clusters, Terraform infrastructure, CI/CD and GitOps pipelines, GPU workloads, model gateways, vector databases, tracing, and evaluation services. Develop reference agents and LLM application templates, support application teams integrating workloads, troubleshoot platform reliability, automate operations, and create runbooks. The role requires Chicago office attendance three days weekly and up to 20% domestic travel.

Build and productionize governed AI applications using Claude, Gemini, RAG, embeddings, vector stores, skills, plug-ins, and MCP. Establish enterprise standards for AI assets, access control, evaluation, monitoring, logging, and reliability. Partner with business, security, cloud, data, and engineering teams to deliver production-ready capabilities in a regulated environment, while providing support and documenting technical decisions.

Lead the technical direction, architecture, and development of Compass, an LLM-powered agent platform. Design multi-agent systems, MCP and A2A integrations, orchestration, evaluation, and observability infrastructure. Drive cross-team technical projects, production support, engineering quality, mentoring, and adoption of AI-assisted development practices. Partner with Product, QA, Design, Operations, and Engineering stakeholders to deliver reliable agentic systems.

Leads the strategy, design, and deployment of AI and machine learning solutions for tax-focused client engagements. Builds Python-based systems for text analytics, extraction, and sentiment analysis; oversees data preparation, feature engineering, model tuning, and platform integration. The Director develops client relationships, drives business development, mentors teams, promotes responsible AI adoption, and ensures governance and professional standards across initiatives.

Lead enterprise AI tooling strategy, governance, roadmap, and lifecycle. Maintain an enterprise inventory of AI tools, manage vendor relationships, drive tool evaluation/onboarding/retirement, ensure security and Responsible AI alignment, and enable cross-functional adoption and optimization across engineering, product, security, procurement, and governance teams.

Leads enterprise AI innovation through hypothesis-driven pilots, evaluation, and experimentation with agentic workflows, RAG, text-to-SQL, and emerging LLM techniques. Designs secure, observable, cost-aware AWS architectures, develops reference implementations, and transitions successful prototypes into scalable enterprise patterns. Communicates technical findings, business impact, tradeoffs, and recommendations while evaluating AI tools, frameworks, vendors, and research.

Build and deploy reliable AI automations for tax and audit professionals. Responsibilities include understanding client workflows, designing controlled AI solutions, creating prompts and agents, developing prototypes with Python and low-code tools, validating accuracy through expert datasets and test harnesses, demonstrating solutions to clients, and collaborating with domain, editorial, product, and engineering teams. The role requires substantial tax or audit experience, professional judgment, and the ability to translate complex workflows into practical AI applications.

Build and operate full-stack features for an AI-powered consulting platform. Responsibilities include frontend and backend development, data pipelines, APIs, LLM-based extraction, retrieval, chat, content generation, AI coding workflows, testing, evaluations, and quality checks. The role owns platform areas end to end, collaborates with consultants, product, and design, and translates expert judgment into reliable AI-assisted functionality.

Builds firmwide AI infrastructure and production-grade generative AI applications, including agentic workflows using RAG, semantic search, planning, and structured outputs. The role develops evaluation harnesses, guardrails, approval workflows, tool-use controls, source attribution, and kill switches while optimizing reasoning quality, retrieval accuracy, latency, cost, safety, and user experience. It also evaluates foundational and open-weight models for enterprise use.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Build and operate LLM-powered features, agentic workflows, NLP pipelines, and event-driven backend services for insurance claims. Responsibilities include designing evals and guardrails, implementing semantic search and structured outputs, modeling relational, document, and graph data, tracing model calls, enforcing privacy and security, writing technical designs, and participating in on-call support.

Owns the architecture, delivery, and operation of LLM-powered systems and agentic workflows for insurance customers. Responsibilities include designing NLP and retrieval pipelines, evals and guardrails, durable event-driven systems, tracing, auditing, privacy, security, and tenant isolation. The role also authors technical designs, reviews code, mentors engineers, manages production operations and on-call, and collaborates with product managers, data scientists, and customers.

Design, build, deploy, and support production AI applications for complex enterprise workflows. Develop LLM, retrieval, agentic, API, and distributed-service capabilities; create evaluation, testing, tracing, and observability practices; investigate AI failures; and optimize quality, security, performance, maintainability, and cost. Collaborate with domain experts, architects, data teams, and software engineers to translate specialized knowledge into reliable, explainable, and maintainable solutions.

Design, build, deploy, and support enterprise generative AI products using LLMs, RAG, agentic workflows, Python, Azure, and cloud-native technologies. Develop APIs, vector search solutions, evaluation and observability systems, and production services on AKS. Translate business needs into scalable product capabilities while balancing quality, latency, cost, security, and reliability. Collaborate with product, architecture, infrastructure, database, operations, and engineering teams on delivery, troubleshooting, incident response, and ongoing enhancements.

Architect, deploy, operate, and monitor on-premises LLM infrastructure and agentic workflows. Build RAG pipelines, MCP servers, data pipelines, CI/CD integrations, testing environments, tracing and evaluation tools, and model-serving network requirements. Support Kubernetes-based AI/ML infrastructure and software development environments while collaborating with cross-functional teams. The role requires a bachelor's degree with four years of related experience or a master's degree with two years, plus TS/SCI clearance.

Leads architecture, deployment, monitoring, automation, governance, and lifecycle management for production machine learning systems. Manages datasets, ML pipelines, model registries, evaluation, drift monitoring, testing, rollback, security, compliance, and audit documentation. Partners with data, cloud, software, cybersecurity, testing, operations, and government stakeholders to deliver secure, traceable, mission-critical ML capabilities.

Build and operate production machine learning capabilities, including dataset management, automated training and deployment pipelines, model monitoring, drift detection, governance, testing, rollback, and secure cloud integrations. Collaborate with data, software, cloud, cybersecurity, testing, operations, and government teams. Maintain traceability, documentation, auditability, incident response, and human-review controls across the model lifecycle.

Designs and secures AI and cloud solutions for government operations, including knowledge assistants, retrieval workflows, bounded agents, APIs, integrations, authentication, authorization, audit logging, testing, monitoring, and human-approval controls. Translates mission requirements into scalable architectures and supports documentation, security compliance, troubleshooting, deployment, and technical guidance. Requires active Secret clearance, a relevant bachelor’s degree, and at least 10 years of experience.

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.

Build and deploy production AI tools, agents, internal platforms, workflow automations, custom demos, and technical assets for Stripe’s Solutions Architecture organization. Collaborate with Solutions Architects, Product, Engineering, and leadership to identify productivity opportunities, translate ambiguous needs into deliverables, and scale successful workflows. Debug backend systems, integrate AI capabilities, document solutions, and maintain infrastructure across multiple codebases.

Build and operate production services for AI agents, including internal tools, customer integrations, and reusable primitives for meetings, browsing, search, email, and payments. Design reliable agent interfaces and workflows, collaborate with product and engineering teams, and improve platform safety, observability, testing, security, and operational reliability.

Build and operate production LLM systems using Ollama and llama.cpp, develop RAG pipelines and MCP integrations, and create Python backend services for AI features. Deploy and maintain workloads across Azure and AWS using Docker and CI/CD. Automate operations, process structured data, support Kafka-based architectures, troubleshoot across the stack, and progressively develop expertise in model quantization, GPU tuning, and secure offline deployments.