Top AI Engineer Jobs in Chicago, IL

The ERP AI Engineer - Manager at PwC leads teams in AI/ML engineering, manages client relationships, and designs AI solutions, all while mentoring team members and addressing complex business challenges.

The AI Engineer Manager leads the Data and Analytics Engineering team in developing AI solutions, manages client accounts, analyzes data, and fosters team leadership and relationships with clients.

As a Senior AI Engineer, you'll design and implement AI-driven solutions, focusing on full-stack development and production-grade applications while mentoring junior team members.

The role involves designing AI systems for mental healthcare, focusing on building scalable, production-grade systems that enhance patient engagement and clinical operations. Responsibilities include setting technical standards and ensuring safety and reliability while working with advanced AI technologies.

The Applied AI Developer will design and develop AI applications, build infrastructure for agentic AI systems, integrate APIs, and collaborate with stakeholders on AI solutions.

Design, build, and deploy LLM-powered applications, collaborating with Data Science teams to ensure agent systems operate reliably in production. Handle full lifecycle from prototype to production, integrating APIs and managing workflows.

As a Staff AI Engineer, you'll lead AI product development, overseeing technical direction, system deployment, and model evaluation while collaborating across teams.

Rapidly prototype and validate AI solutions for advisor workflows: integrate models, signals, and UX into experiments, test feasibility and performance, iterate with user feedback, document technical learnings, and hand off prototypes for productionization.

As a GTM AI Engineer, design and execute AI-driven workflows for Superhuman's go-to-market stack, improving automation in sales processes and team collaboration.

Design, build, and ship production-grade agentic AI workflows using LangGraph, Temporal, and Pydantic. Implement AI observability with LangFuse, set AI engineering standards (RAG, prompt management, tool-calling), benchmark LLMs, deploy cloud AI (containerization, cost management), and mentor engineers while supporting enterprise SLA, security, and compliance needs.

This role involves defining AI architecture, improving system quality, leading teams, and building production-ready AI systems at Drata.

As an AI Engineer, you will build and scale AI business applications, focusing on generative AI. Responsibilities include rapid prototyping, applying software engineering best practices, and providing production support.

The AI Engineer role involves prototyping AI solutions, collaborating with cross-functional teams, validating workflows, and documenting technical learnings.

The Senior AI Engineer will lead AI adoption, experiment with solutions, package workflows, measure impact, and drive collaboration in engineering.

As a Principal AI Engineer, you'll design production-grade systems integrating generative AI in mental healthcare, overseeing architecture and applied research.

Lead the development of innovative AI solutions, guide teams through complex challenges, and collaborate with diverse stakeholders to enhance operational effectiveness and achieve strategic objectives.

Lead the design and implementation of enterprise AI/ML solutions, oversee project performance, mentor leaders, and ensure compliance with Responsible AI.

The role involves designing and implementing AI/ML solutions, leading teams, mentoring staff, and managing project delivery while ensuring client satisfaction.

Lead technical strategy and hands-on engineering for large-scale data ingestion and processing platforms supporting physical AI and autonomy. Design cloud-native, streaming and batch pipelines for LiDAR, radar, video, imagery, and telemetry. Drive architecture, platform standards, CI/CD, observability, and cross-team agile delivery while mentoring engineering teams and enabling ML, computer vision, and autonomy use cases.

Build and deploy production-scale AI/LLM systems: own ML lifecycle from model development to serving, optimize GPU/Kubernetes deployments, design LLM applications and APIs with monitoring, apply CI/CD and version tracking, collaborate with product teams, and contribute to AI engineering best practices.

Lead design, development, and production deployment of AI/ML solutions and conversational chatbot platforms. Own end-to-end implementation, MLOps/CI-CD, containerized deployments, solution architecture, security and governance, mentor engineers, and collaborate with product, enterprise architecture, and vendors to deliver scalable, monitored AI systems.

The role involves designing and implementing AI and machine learning solutions, transforming data into insights, and supporting client engagements. Responsibilities include model development, data analysis, and enhancing model performance using established technologies.

Own production GenAI systems end-to-end: design and deploy agentic workflows, RAG pipelines, and FastAPI services; build evaluation pipelines and monitoring; integrate LLM agents with policy/claims systems; ensure observability, safety, and error handling; and automate repetitive operational tasks.

Design and implement AI capabilities, transition prototypes to production, build agent workflows, optimize data retrieval, and automate evaluation processes.

Lead the design and delivery of scalable voice and chat solutions using Amazon Connect, focusing on integrations, reliability, and performance optimization.