Top AI Engineer Jobs in Chicago, IL

Lead the AI engineering roadmap for Digital Operations, delivering end-to-end AI products (LLMs, RAG, copilots, agents) from prototype to production. Integrate AI with enterprise data, marketing systems, and cloud platforms, build reusable frameworks, ensure governance and monitoring, and drive adoption through pilots and measurable business impact.

Hands-on technical leader who designs, builds, and deploys production-grade AI automation and agentic workflows. Responsibilities include rapid prototyping, RAG and document-intelligence systems, API integrations, orchestration, monitoring, reusable AI assets, and mentoring a small engineering team to deliver frequent releases and measurable business impact.

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.

Take Deepgram's speech models and optimize, compile, and run them on resource-constrained embedded and edge hardware. Work across model compression, runtime performance (C/C++/Rust), RTOS/bare-metal and embedded Linux environments, vendor NPUs/DSPs and inference runtimes, benchmarking, deployment pipelines, OTA updates, and partner with silicon vendors to enable real-time, low-power on-device voice experiences.

Own end-to-end voice platform migration and scaling: LiveKit migration, real-time turn-taking, VAD, SIP/WebRTC, multi-agent handoff, training/fine-tuning voice models, inference cost optimization, and retrieval-based grounding using Supabase/pgvector.

Lead design and deliver SAP BTP AI solutions for Finance. Partner with SAP Finance teams to identify AI use cases, build GenAI capabilities (RAG, Joule Skills, agents), ensure secure, scalable integrations, validate feasibility via POCs, and produce production-ready solutions for reconciliations, controls, reporting, and financial close.

Rapidly prototype AI solutions embedded in advisor workflows: integrate models, signals, APIs and UX into experiments, validate feasibility and performance, and hand off documentation to production teams.

Design and scale a reusable Customer Success AI platform: build foundational AI infrastructure (orchestration, memory, context, workflow engines), optimize model routing, observability, governance, and security, lead architecture and standards, mentor engineers, and partner cross-functionally to deliver enterprise-grade, multi-product AI capabilities.

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 and delivery of enterprise integrations, automation, and AI-enabled workflows across SaaS platforms. Architect scalable APIs, conversational IT/self-service experiences, and agentic LLM workflows while partnering with Security, Infrastructure, and Product to ensure governance, reliability, and operational excellence.

Lead design, build, and operate scalable cloud-native data pipelines, microservices, and streaming ingestion on AWS. Partner with architects and stakeholders to define data architectures, enforce data quality and automated testing, monitor production systems, and drive CI/CD and operational excellence for AI and autonomy initiatives.

Design, prototype, and deploy LLM-powered agents, RAG workflows, and automations that accelerate internal teams and customer-facing product features. Integrate AI systems with SaaS tools, build reusable components and evaluation frameworks, embed with business teams to deliver measurable outcomes, and establish standards for responsible AI, monitoring, and cost control across the company.

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.

Design, develop, test, operate, and maintain AI-enabled software and infrastructure. Build production-ready code, RAG pipelines, vector search integrations, and agentic AI components. Follow SRE/DevOps and responsible-AI practices, participate in code reviews, troubleshoot model quality, and collaborate across engineering, product, and business teams to deliver scalable GenAI solutions.

Lead development and deployment of generative AI solutions and assistants using Python, FastAPI, and AWS. Build CI/CD with GitHub Actions, manage infrastructure with CloudFormation, integrate GenAI frameworks (LangChain, LangGraph, Semantic Kernel, Crew.ai), collaborate with data scientists to productionize models, and assist in ML/AI pipelines using Apache Airflow.

Lead product strategy and delivery for Capital Ones AI coding and developer experience tools. Translate frontier AI into governed, developer-loved products that scale AI-augmented workflows, improve code review, embed enterprise standards, and accelerate software delivery through cross-functional leadership and data-driven decisions.

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

Lead design and delivery of production-grade generative AI systems on Azure AI Foundry, integrating structured and unstructured data, implementing RAG/agent patterns, driving semantic architecture, ensuring governance/compliance, mentoring engineering teams, and partnering with stakeholders to embed AI capabilities into client-facing products.

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.

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.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

Lead development and delivery of healthcare AI/GenAI solutions for health systems—architect RAG and agentic workflows, manage global MLOps and model governance, engage executive stakeholders, and drive production-grade use cases across clinical, financial, and operational domains.

Lead development and delivery of AI and GenAI solutions for health systems. Serve as strategic advisor, manage teams, ensure HIPAA-compliant deployments, implement MLOps and model governance, translate clinical workflows into AI use cases, and drive stakeholder alignment and operational excellence.

Design, build, and operate large-scale LLM infrastructure and data platforms for training, fine-tuning, and inference. Provision GPU clusters, optimize GPU utilization, implement model lifecycle management, deploy inference frameworks, create evaluation and observability systems, and collaborate with data scientists to productionize AI capabilities. Mentor engineers and enforce MLOps/DataOps best practices.

Design, build, deploy, and iterate production-ready AI agents and LLM-based solutions that integrate with enterprise workflows. Rapidly prototype, evaluate, and optimize agents for performance, cost, and adoption. Partner across functions to redesign workflows, contribute to an AI Center of Excellence, and ensure responsible, secure, and scalable agent development.