Top AI Engineer Jobs in Chicago, IL

Develop and deploy generative AI assistants, processing pipelines, tooling, and software using Python or Go. Build FastAPI services, edge-device components, CI/CD pipelines, and AWS infrastructure. Apply LLMs, prompt engineering, and modern agent architectures using LangChain and LangGraph. Collaborate with data scientists to productionize scalable analytics and AI solutions, and assist with Airflow-based machine learning pipelines. The role also requires software architecture, testing, technical writing, and cross-functional collaboration.

Ten-week AI engineering internship focused on evaluating AI models, frameworks, and developer tools; prototyping AI-driven automation and assistants; tracking adoption and efficiency metrics; collaborating with traders and engineers; presenting findings to senior leaders; and addressing complex problems in algorithmic trading.

Leads AI and data-driven solutions for AML and sanctions clients in banking and capital markets. Responsibilities include developing AI solutions with Python, managing data architecture and pipelines, overseeing analytics and visualization deployments, guiding advanced analytics teams, engaging stakeholders, validating client outcomes, improving processes, and coaching diverse teams through complex situations.

Leads AI technology integration and deployment within the tax practice. Oversees data infrastructure, pipelines, modeling, quality validation, and multi-source integration. Develops Python-based analytics solutions using machine learning and natural language processing, conducts rapid experimentation, and drives data-informed innovation. Coaches and develops high-performing teams while collaborating with stakeholders and advising on strategic initiatives.

Leads AI engineering and advanced analytics initiatives for AML and sanctions clients. Develops scalable data, visualization, and analytics solutions; validates data integrity, security, and compliance; identifies system linkages and optimization opportunities; manages client relationships and project execution; and mentors teams delivering innovative AI solutions.

Lead design and delivery of advanced data and ML solutions, performing exploratory analysis, statistical modeling, and visualization to inform strategy. Manage and coach teams, collaborate with clients to validate outcomes, and improve analytics processes and data quality to drive business decisions.

Leads the design, development, deployment, and optimization of scalable AI and generative AI solutions. Translates business needs into secure, production-ready architectures; applies foundation models, NLP, retrieval, vector search, and agentic workflows; and implements MLOps and LLMOps practices. Manages project workstreams, evaluates solution performance, ensures security and compliance, collaborates with stakeholders, and coaches junior team members.

Lead design and delivery of scalable AI/ML systems: data wrangling, model training, deployment, and infrastructure. Manage teams, mentor staff, own project planning/budgeting, and collaborate with stakeholders to drive AI-driven business insights.

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.

Design, build, and deploy AI and machine learning solutions at scale. Develop data pipelines and scalable models, perform data wrangling and analysis, apply NLP and neural networks, collaborate with clients and teams, and contribute to open-source and iterative improvement of AI systems.

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.

Lead an agile engineering team to architect, develop, and productionize secure, scalable software that integrates LLMs and generative AI. Drive AI-assisted engineering practices, mentor engineers, translate business requirements into technical roadmaps, automate reliability, and ensure compliant, high-quality delivery.

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.

Provides global technical leadership for Physical AI and cyber-physical product assurance. Develops standards, certification programs, assessment methodologies, testing criteria, and customer-facing services for robotics, autonomy, drones, smart mobility, and connected intelligent products. Evaluates real-world system behavior, hazards, failure modes, safety controls, human interaction, robustness, and lifecycle changes. Advises customers, regulators, and standards bodies while building global evaluator training, delivery guidance, and scalable assurance capabilities.

Provides global technical leadership for digital AI standards, certification, assurance, training, advisory, and assessment services. Develops AI governance and lifecycle assurance methodologies covering predictive and generative AI, LLMs, agents, cloud AI, model risk, testing, transparency, fairness, privacy, security, robustness, and operational resilience. Translates regulations, standards, emerging technologies, and customer needs into scalable offerings while engaging customers, regulators, standards bodies, and internal stakeholders.

Design, develop, deploy, and support AI solutions including generative AI applications, agents, workflow automation, document intelligence, and retrieval-augmented generation. Partner with business and IT teams to identify use cases, integrate enterprise platforms and data sources, establish prompt and evaluation standards, support AI governance and security, monitor solution performance, and train employees on responsible AI adoption.

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.

Architects and develops AI-powered systems, tools, agents, backend services, APIs, and experimentation frameworks using foundational AI models. Integrates AI capabilities with microservices, AWS, Snowflake, and complex data systems while ensuring scalability, reliability, and maintainability. Translates business problems into technical solutions, prototypes AI applications, collaborates with product and engineering teams, evaluates emerging AI technologies, and documents system designs and integration patterns.

Build and ship production AI systems across retrieval, knowledge graphs, inference, agentic workflows, and data platforms. Develop customer-context retrieval, scored signals, evaluation and observability capabilities, and workflows that turn insights into action. Work hands-on with large and small models, durable orchestration, cloud deployment, inference-cost optimization, and high-volume observability data. Establish reusable engineering patterns, collaborate with product and platform teams, and mentor through strong technical contributions.

Design, build, and operate agentic AI systems end-to-end for Professional Services. Translate stakeholder needs into secure, scalable multi-agent architectures, define performance benchmarks, manage deployments and incident response, advise leadership, mentor teammates, and maintain runbooks and production tooling including on-call responsibilities.

Build and productionize agentic workflows, RAG pipelines, AI services, backend systems, and ML pipelines for sales automation. Integrate Salesforce, quoting engines, data lakes, and internal platforms using scalable, event-driven architectures. Lead 0-to-1 development of tools for account research, lead prioritization, and quoting while ensuring reliability, observability, performance, and resilience across the GTM technology stack.

Leads the design, development, deployment, and lifecycle management of enterprise Generative AI solutions using LLMs, RAG, agentic workflows, and cloud platforms. Builds scalable Python microservices with Docker, Kubernetes, and infrastructure-as-code tools across Azure and AWS. Establishes GenAIOps, monitoring, security, CI/CD, and governance practices while providing architectural leadership, mentoring engineers, and partnering with product, business, risk, and security stakeholders.

As a Software Engineer, you will build AI systems to enhance developer workflows, implementing agents and servers, and ensuring code reliability through evaluation pipelines.

Build and own conversational voice AI products end-to-end: architect, implement, and deploy the platform. Design infrastructure, develop front-end and back-end systems, deliver reliable customer outcomes, and collaborate directly with the CTO to scale the product for enterprise customers.

Build and operate LLM-powered features and agentic workflows for insurance claims using Python and TypeScript. Responsibilities include developing NLP pipelines, evals, guardrails, event-driven services, durable workflows, observability, data models, and privacy and security controls. The role owns AI features end to end, from design and collaboration through production, monitoring, on-call support, and continuous improvement.