Top AI Engineer Jobs in Chicago, IL

Design, build, and operate a secure, scalable Generative AI platform on AWS (including Bedrock) to support production LLM applications in regulated financial environments. Own platform lifecycle: architecture, deployment, operations, monitoring, incident management, reliability, and performance. Implement LLM inference, RAG, Agents, guardrails, observability, security controls, access management, compliance, and cost optimization. Provide production support, reliability engineering, and AI governance.

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 responsive React + TypeScript frontends and scalable Python/Rust backend services and APIs. Integrate with cloud (AWS/GCP/Azure), design databases, CI/CD, tests, and monitor reliability, performance, security, and cost. Optionally develop LLM/RAG/embeddings/vector database AI features.

Build end-to-end AI-native features: responsive React/TypeScript frontends, Python/Rust backends, cloud integrations (AWS/GCP), databases, CI/CD, observability, and optionally LLM/RAG/embeddings-based AI features while improving reliability, performance, security, and cost.

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 and productionize end-to-end AI-powered product features: prototype and iterate on prompts, retrieval flows, evals, backend systems, APIs, data models, and UX to deliver reliable, customer-facing LLM workflows.

Hands-on Senior AI Engineer to design and ship production Generative AI systems: build RAG pipelines, embeddings, vector search, multi-agent orchestration, backend APIs (Python/Flask/FastAPI), deploy cloud-native workloads, implement LLMOps practices, and mentor through code reviews while owning end-to-end delivery.

Lead architecture, design, and delivery of enterprise-scale Agentic AI solutions. Oversee multiple engineering pods, mentor teams, manage client-facing architecture discussions, ensure production-grade MLOps, cloud infrastructure, microservices, observability, and adoption of emerging AI technologies.

Design, build, and operate agentic LLM workflows for delivery: requirements review, automated checks, early risk detection, RAG-backed knowledge bases, human-in-the-loop feedback, monitoring, and reusable components from prototype to production.

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.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

Senior SRE on the IT Operations team owning reliability, monitoring, and incident response for AI infrastructure. Build automation, CI/CD and Kubernetes tooling, improve observability and documentation, and develop internal full-stack tools using Go or Python. Partner with Infrastructure, Security, and Compliance to scale secure, resilient AI deployment pipelines.

Design and build AI systems for finance, automating workflows and ensuring high performance standards while delivering solutions rapidly to private equity clients.

Build production-grade infrastructure for GTM AI agents: harden prototypes, write evals and prompts, implement observability, own deployment pipelines on GCP, manage safe database access, monitor and improve agent fleet, and partner with GTM builders to scale reliability.

Design, implement, and maintain core infrastructure and tooling to enable agentic AI development. Drive AI enablement, developer relations, training, and shared platform tooling to support engineering teams and production LLM/agent integrations.

Lead design and delivery of scalable Amazon Connect voice and chat solutions. Build Lambda-based integrations, integrate Lex bots and external systems, optimize routing/queues, ensure high availability and monitoring, lead production support, RCA, and continuous improvement for contact center platforms.

Build and maintain AWS-based AI infrastructure and services for ML workflows and real-time inference. Implement CI/CD pipelines, integrate LLM/GenAI components, ensure observability, write high-quality code, collaborate cross-functionally, and prototype ML algorithms to turn forensic data into investigation leads.

Fine-tune and align LLMs for specialized financial tasks; build agentic solutions with LangChain/Autogen; engineer ML pipelines for large financial datasets; deploy, monitor and scale production models; track AI research and integrate emerging tooling.

Design, build, and operate a secure, scalable Generative AI platform on AWS (Bedrock) for production LLM applications in regulated environments. Own architecture, deployment, monitoring, incident management, reliability, governance, cost optimization, and AI evaluation/Responsible AI practices.

Design, build, and own scalable data pipelines and evaluation systems that power production AI features and internal analytics. Ensure data quality across ingestion, modeling, and reporting, collaborate with ML and analytics teams, deploy and monitor ML systems, and establish standards for data work and evaluation.

Design, build, deploy, and optimize AI/ML models and pipelines (including LLMs and RAG). Integrate models into production, work with cross-functional teams, ensure monitoring and compliance, and contribute to scalable AI systems and automation.

Design, build, fine-tune, and deploy production-grade AI and ML systems (LLMs, RAG). Develop scalable APIs, integrate AI into business systems, monitor model performance, and collaborate with cross-functional teams.

Build production-ready agentic AI applications that integrate agents, workflows, memory, reasoning, and tools to solve customer use cases. Rapidly prototype, iterate, and ship end-to-end AI experiences; design integrations and UX, ensure reliability, performance, and evaluation; partner with platform and product teams to create reusable components and customer-ready solutions.

The Senior AI Engineer will design and build autonomous AI agents for mental health applications, focusing on LLM integration and reinforcement learning techniques for user interactions.

Lead design and productionalization of AI-driven features for care navigation and PBM. Rapidly prototype and ship full-stack solutions using Node.js, React, TypeScript, Ruby, GraphQL, and Postgres. Architect scalable, secure systems for sensitive healthcare data, set AI integration standards, drive technical decisions, and mentor engineers to ensure production-quality implementations.