Top AI Engineer Jobs in Chicago, IL

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.

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.

Early-career software engineer joining Morningstar’s Central Technology AI team through its development program. Responsibilities include contributing to Agile projects, researching and solving business problems, building AI-enabled solutions with Python and modern AI tools, collaborating with engineers and business stakeholders, presenting outcomes, and participating in structured training and mentorship. The role is based in Chicago with four days per week in the office.

Build and deploy governed AI agents, automations, and multi-agent systems for business teams. Responsibilities include identifying AI opportunities, translating needs into requirements, prototyping, integrating enterprise systems, instrumenting telemetry, measuring ROI, monitoring risks and costs, and applying governance controls. The role also develops reusable patterns, documents solutions, enables business users, and communicates lessons learned to the AI Center of Excellence.

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.

Build and deploy generative AI solutions, including APIs, MCP servers, AI agents, LLM integrations, and full-stack applications. Develop scalable AWS cloud services, observability systems, evaluation pipelines, and production agent workflows. Collaborate with Product, UX, Security, Legal, and engineering teams to design interfaces, adapt AI tools to company codebases, and support customer experience and developer productivity use cases.

Leads AI engineering initiatives for tax technology, designing and deploying machine learning solutions using Python, deep learning, and NLP. Builds data pipelines, validates models, guides experimentation, and translates tax business needs into scalable production systems. Oversees complex workstreams, directs cross-functional teams, presents technical recommendations, coaches team members, and ensures solutions meet technical, client-service, policy, and responsible AI standards.

Develop and operate autonomous AI agents and enterprise integrations using Databricks, Claude, MCP, and event-driven architectures. Build Databricks Apps, maintain MCP connectors, monitor agent performance, manage incidents, and document workflows, triggers, dependencies, and data sources. Partner with business, governance, and process teams to identify suitable agentic AI applications, particularly for go-to-market systems, while applying logging, auditing, naming, and change-management standards.

Build, test, deploy, and evaluate machine learning models using established frameworks. Support feature engineering, data preprocessing, automated training and monitoring pipelines, and production AI integration. Collaborate with engineering and product teams to define AI requirements, research performance improvements, and respond to critical IT incidents. The role requires advanced quantitative education, 5+ years of AI/ML engineering experience, Python expertise, and knowledge of deep learning and generative AI.

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.

Defines and implements enterprise AI observability, telemetry, evaluation, drift detection, guardrails, incident management, governance controls, audit evidence, and cost monitoring. The role establishes standards for internally built and vendor AI systems, integrates monitoring and compliance platforms, maintains the AI system inventory, and coaches engineering teams on production adoption. It partners with Risk, Compliance, Legal, Privacy, and Security to operationalize AI governance in a regulated environment.

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 own evaluation infrastructure, observability tooling, datasets, annotation workflows, and diagnostic systems for production AI agents. Lead experiments across prompts, models, routing, caching, cost, and latency. Diagnose failures across planning, tool use, and agent execution, while partnering with AI Core engineering. Manage and grow the AI Quality team as a hands-on player-coach, contributing directly to production code and technical direction.

Analyzes complex data sets, develops Python-based data solutions and pipelines, and applies advanced analytics to support client strategy and business performance. Designs dashboards and visualizations, collaborates with cross-functional teams, manages stakeholders, improves data quality, and builds client relationships. The role also involves solving complex business challenges, identifying industry trends, and delivering high-quality work within PwC’s Risk & Regulatory practice.

Build and operate Affirm’s AI platform, including LLM and MCP gateways, agent runtimes, evaluation services, and governance controls. Design highly available backend systems, manage identity, quotas, routing, caching, reliability, observability, and security for AI workloads. Own projects from design through launch and on-call, establish engineering standards, collaborate across teams, mentor engineers, and help scale AI systems into production.

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.

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.

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.

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 global teams developing production AI, GenAI, RAG, agentic workflow, and MLOps solutions for health systems and health plans. Translates clinical, financial, and operational challenges into HIPAA-compliant use cases involving clinical decision support, population health, revenue cycle, research, and optimization. Oversees healthcare data science engagements, model governance, executive stakeholder communications, client delivery, and team development.

Designs, develops, deploys, and scales advanced AI and machine learning models and systems. Leads research initiatives, architecture decisions, technical design reviews, and continuous improvement efforts. Collaborates with data scientists, software engineers, and product managers, while mentoring engineers and communicating strategies to executive stakeholders. Applies data mining, modeling, NLP, deep learning, and statistical techniques to solve complex product and systems problems.

Build and deploy AI-powered tools for Rula’s business and clinical teams by observing workflows, rapidly delivering production software, measuring usage and impact, and enabling teams to operate tools independently. The role requires privacy-by-design practices, rigorous AI evaluation, customer-centric discovery, and integration with complex legacy systems. Staff engineers may optimize LLM costs and quality while creating reusable building blocks for future teams.

Owns architecture, delivery, evaluation, security, and operation of LLM-powered systems and agentic workflows for insurance customers. Designs NLP pipelines, durable event-driven systems, deterministic orchestration, tracing, auditability, privacy controls, and tenant isolation. Establishes evaluation and guardrail strategies, reviews designs and code, authors technical documentation, mentors engineers, collaborates with product and data science teams, and participates in on-call operations.

Build and operate LLM-powered features and agentic workflows for insurance claims using Python and TypeScript. Develop evaluations, guardrails, retrieval and NLP pipelines, durable event-driven services, observability, and secure multi-tenant architectures. Work across relational, document, and graph databases, cloud platforms, and workflow engines. Own features from design through production, participate in on-call, and collaborate with product and data science teams.

Develop and deploy AI solutions, including machine learning models, generative AI applications, agents, and data pipelines. Partner with business teams to identify automation and decision-support opportunities, monitor production performance, optimize solutions using feedback, and establish reusable tools, development standards, and best practices. The role requires Python and SQL proficiency, hands-on AI experience, and strong communication skills.