Top AI Engineer Jobs in Chicago, IL

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.

Lead architecture and delivery of AI-native features for a multi-tenant SaaS platform. Drive cross-functional technical initiatives, design scalable microservice systems, integrate LLMs and agent frameworks, set engineering standards, and mentor engineers to elevate technical execution.

Design, build, deploy, and maintain scalable, resilient backend services for a Voice AI platform. Lead cross-cutting projects, make architectural decisions, integrate AI platforms, implement observability and evaluation frameworks, and mentor engineers.

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

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.

Lead architecture and implementation of a patient-controlled health data platform: custody, consent management, secure EHR integrations, AI-native workflows (LLMs, agents, RAG), mobile-first patient experiences, and build the engineering culture and hiring bar as a founding technical partner.

Design, build, and ship AI-powered infrastructure, internal tools, and workflows for GTM teams from concept to production. Partner with Ops, Analytics, Product, and field SMEs to identify friction, automate GTM processes, and iterate on scalable AI/LLM solutions.

Design, build, and deploy enterprise generative AI solutions using LLMs, RAG, vector search, OCR, and AI agents. Develop cloud-native, Kubernetes-based services, REST APIs, and integrations; contribute to CI/CD, IaC, and architecture reviews; mentor team members and ensure security, observability, and software quality.

Build and scale AI platform infrastructure for internal agents across AWS/GCP. Integrate identity (Okta, OAuth2/OIDC), implement agentic security, containerized deployments (Docker/Kubernetes), CI/CD and Terraform-based pipelines, run vendor evaluations, and design observability/telemetry for LLM-driven workflows while collaborating with security, identity, and engineering teams.

Build end-to-end full-stack AI features on a Ruby on Rails backend and React frontend, design and run experiments (A/B tests and AI evaluations), implement AI infrastructure for reliable LLM inference, and iterate quickly to scale production-ready LLM-powered products.

Build the core AI platform and high-performance Rust backend powering an AI-native trading copilot. Implement streaming responses, low-latency tool execution, caching, orchestration of models and tools, secure APIs, auditing/tracing, and safe, auditable trading action infrastructure. Partner with product and frontend to enable AI-native UX and translate trading domain requirements into resilient platform primitives.

Lead ownership of reliability, evaluation, and safety for an AI copilot in a trading platform. Build eval systems, benchmarks, automated quality gates, monitoring, and model improvement loops; partner with engineering and product to define safe tool contracts and ensure predictable, auditable assistant behavior in trading workflows.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

Design, build, and operate AWS-native AI/ML systems: RAG pipelines, fine-tuned LLMs, and microservices. Implement IaC (Terraform), CI/CD, policy guardrails, and production deployments integrating Bedrock/SageMaker, LangChain, and telemetry for secure, scalable enterprise use.

Lead the product strategy for Scribd's internal developer and agentic platforms: unify AI workstreams, own internal app/plugin/agent runtimes, translate functional needs into platform requirements, drive cross-functional adoption and metrics, and run internal rollouts and evaluation to prove impact.

Design, build, and operate production-grade AI systems (LLM applications, agent platforms, evaluation frameworks) using Python and modern AI frameworks. Translate ambiguous business problems into scalable, reliable AI solutions, define success metrics and evaluation pipelines, integrate with internal systems and third-party services, and continuously monitor and improve performance, reliability, cost, and safety in production.

Lead design and delivery of enterprise and generative AI solutions, build scalable AI enablement frameworks and reusable accelerators, drive adoption of LLMs, RAG, agentic AI and automation, provide platform and governance guidance, mentor teams, and align AI roadmap with risk, compliance, and business priorities.

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

As a Senior AI Engineer, you'll implement AI solutions, facilitate customer workshops, lead development projects, and align strategies with business objectives, utilizing Azure and OpenAI technologies.

The AI Engineer will develop and deploy AI/ML models, optimize data pipelines, and collaborate with teams to enhance investment decision-making using AI technologies. Responsibilities include maintaining AI governance and facilitating innovation workshops.

Build and deploy production-quality AI/LLM agents, internal tools, and automated workflows to boost Solutions Architecture productivity; collaborate with SAs and cross-functional teams, harden prototypes into scalable systems, and maintain backend systems and demo assets.

Lead design and build of an enterprise AI platform enabling GenAI and ML solutions: define roadmap, deliver POCs, implement production-grade platform capabilities (IaC, monitoring, FinOps), collaborate with architecture/cloud/security, mentor engineers, and accelerate deployment and scaling of AI use cases.

Build and iterate fast AI prototypes for advisor workflows, integrating models, signals, APIs, and UX. Test feasibility and performance, capture technical learnings, and produce handoff documentation for production teams.

Lead Conversational AI Developer responsible for developing chatbots and virtual assistants, integrating AI technologies, and improving user interactions through data analysis and software development.

Lead design, development, and productionization of ML and LLM systems for patient- and provider-facing healthcare products. Own end-to-end workflows: problem scoping, data, modeling, evaluation, deployment, monitoring, and clinical guardrails. Build evaluation frameworks, infrastructure, and tooling; partner with clinicians and product; mentor engineers and set technical direction to deliver safe, impactful AI features for clinical care.