Top AI Engineer Jobs in Chicago, IL

Build and deploy production AI features for an EHR platform, including LLM integrations, automations, data workflows, APIs, evaluations, monitoring, and HIPAA-compliant PHI handling. Partner directly with clinic customers and internal teams to prototype, implement, and harden solutions. Responsibilities include system design, clinical data integration, production debugging, AI tool reviews, and translating field insights into working software.

Designs and develops secure, performant Copilot products and platform services across Windows, Mac, and mobile. Builds distributed AI and agent-driven systems capable of reasoning, planning, and executing tasks. Collaborates with infrastructure, platform, application engineering, and research teams to ship high-quality software. Champions AI-assisted development, coding-agent adoption, prompt design, tooling best practices, and human-in-the-loop systems.

As an AI Process Forward Deployed Engineer, you will deploy enterprise workflows, improve systems, drive adoption, and collaborate across teams to enhance AI integration in business processes.

Develop and optimize GPU kernels, compilers, runtimes, and distributed inference systems for large language model serving. Improve latency, throughput, reliability, and hardware efficiency through profiling and low-level performance optimization. Integrate scalable improvements into production systems, collaborate across model, infrastructure, compiler, and hardware teams, and mentor engineers. Principal-level contributors additionally lead cross-stack initiatives and create reusable performance capabilities across models and hardware generations.

Build and operate production AI agent systems using Python, LangChain, LangGraph, and Amazon AgentCore. Develop agent tools, Snowflake integrations, retrieval workflows, structured outputs, evaluations, guardrails, and observability. Deploy, monitor, and improve reliable LLM-powered features while collaborating with data engineers. The role requires production Python experience, hands-on LLM development, evaluation framework design, and familiarity with modern AI tooling. Periodic travel may be required.

Designs, builds, secures, and governs enterprise-scale agentic AI systems. Responsibilities include developing LLM, RAG, prompt engineering, and multi-agent solutions; implementing defenses against prompt injection, jailbreaking, and tool misuse; creating resilient, observable, auditable pipelines; and applying software engineering, CI/CD, infrastructure-as-code, and microservices practices. The role also leads cross-functional alignment, advises executives, develops architecture roadmaps, and supports AI governance in regulated environments.

The AI Engineer designs, develops, and operationalizes responsible AI systems, including generative AI applications, RLHF models, guardrails, prompt engineering, and RAG frameworks. Responsibilities include evaluating and fine-tuning models, developing Python applications on cloud platforms, implementing ML software engineering practices and CI/CD pipelines, and collaborating with stakeholders on AI roadmaps and delivery. The role requires leading complex AI/ML initiatives and translating technical concepts for diverse audiences.

Own production agentic AI systems that audit medical claims. Translate ambiguous business problems into measurable technical initiatives, define evaluation metrics, architect agent systems, build reliable Python software, and manage accuracy, latency, cost, and citation grounding. Lead technical direction, review designs, mentor engineers, collaborate with clinical and business stakeholders, and present defensible results. The role requires hands-on experience with agent SDKs, evaluation harnesses, long-context systems, and AI development tools.

The Principal AI/ML Engineer will lead the development of AI/ML platforms, drive technology architecture, ensure quality and integration, and provide strategic leadership while managing a team of engineers.

Build and operate production AI capabilities across enterprise platforms, including agent runtimes, model routing, RAG, tool calling, guardrails, evaluation, monitoring, and cost and latency optimization. Develop reliable, measurable, safe, and cost-conscious AI systems; create evaluation frameworks and quality controls; monitor production behavior; and collaborate with backend, platform, product, security, and client teams to deliver practical AI solutions.

Design, implement, and operate production-quality AI framework components, benchmark and optimize large model training and inference across GPUs and Microsoft hardware, build automation and observability, drive cross-team projects from definition to deployment, and provide technical leadership and mentorship for improvements in performance, reliability, and model onboarding velocity.

Build, test, and support AI-powered applications and workflow automations using Microsoft Power Platform, GPTs, agents, APIs, and automation tools. Configure prompts, integrations, and knowledge sources; troubleshoot workflows; conduct testing and UAT; document solutions; support deployments and users; and follow information security practices. The role offers guided development for an early-career engineer with foundational programming, AI, automation, and software skills.

Architect, build, and deploy production-grade AI/ML platforms and LLM/RAG solutions. Lead system design, CI/CD and Terraform-based IaC, ensure reliability and monitoring, mentor engineers, run incident response, and prototype emerging AI technologies.

Build integrations that enable AI agents to interact with first-party and third-party applications. Develop structured agent actions, information retrieval interfaces, cross-application workflows, reusable SDKs, and reliable asynchronous APIs. Manage authentication, authorization, permissions, and integration limitations while collaborating with Android engineers on system-level application access.

Build agent workflows, orchestration, tool integrations, context retrieval, memory, multi-step execution, and long-running stateful processes. Improve reliability through evaluations and regression testing, integrate local and cloud model providers, and support model routing and inference optimization. The role requires backend or AI application engineering expertise, strong API and distributed systems knowledge, and proficiency in Python, Go, or TypeScript.

Evaluate, deploy, and optimize machine learning models for on-device execution on Android phones. Benchmark models on mobile hardware, optimize latency, memory, and power consumption, and apply quantization, compression, and distillation. Integrate models with Android applications, system services, and AI agent runtimes. Build hybrid local/cloud model routing and determine which workflows should run locally versus through cloud inference.

Build and scale AI agents for sales enablement across frontend and backend systems. Lead development with Next.js, React, TypeScript, Python, Postgres, OpenAI, and FastAPI; design robust AI integrations, engage customers, contribute to roadmap planning, and iterate rapidly in a high-ownership startup environment.

Lead technical vision and architecture for AI frameworks, performance, benchmarking, and tooling. Drive cross-team alignment, mentor engineers, ship high-performance distributed software, and improve model onboarding, runtime performance, reliability, and Azure hardware efficiency.

Develop and optimize AI models for voice processing, natural language understanding, and speech synthesis. Integrate conversational AI into a Voice AI platform, process large datasets, and deploy scalable models on cloud or on-premise infrastructure. Collaborate with product, engineering, and design teams while testing, debugging, and refining AI solutions for reliable, human-like interactions.

Researches and develops AI, machine learning, computer vision, signal processing, and control algorithms for autonomous defense and commercial systems. Builds, trains, evaluates, and optimizes models; develops prototypes and data products; defines requirements; and collaborates across perception, mission planning, simulation, testing, and customer application teams.

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.

Build and deploy AI-powered automation workflows for marketing, growth, content, sales collateral, social distribution, and community operations. Responsibilities include auditing existing processes, automating blog production and publishing, encoding brand voice, integrating platform APIs, implementing human approval steps, documenting workflows, and handing them off to the team.

Supports AI engineering initiatives at Vantor, contributing to spatial intelligence solutions and helping develop technologies that enable customers to understand current and future conditions. The posting provides limited role-specific responsibilities but emphasizes problem-solving, innovation, collaboration, and mission-focused work. Applicants must qualify as a U.S. Person under applicable export control and ITAR requirements.

Lead design and build of an AI-native marketing platform: integrations, multi-agent orchestration, UIs, tooling, analytics, CI/CD, auth/multi-tenancy, and production AI agents to automate marketing workflows and drive adoption across teams.

Build, productionize, and operate AI-driven features and integrations across frontend, backend, and model layers. Prototype and deliver agent-driven experiences (conversational interfaces, RAG, tool-calling), integrate LLMs and external data sources, and collaborate with product and design. Participate in on-call/standby duties.