Top Remote Artificial Intelligence Engineer Jobs in Chicago, IL

Design, build, and deploy AI-powered applications, agents, and copilots using LLMs and RAG. Integrate AI into enterprise systems, implement governance and security controls, optimize prompts and models, and deploy scalable solutions primarily on Azure.

Design, build, and deploy Microsoft Copilot and Power Platform solutions (PowerApps, Power Automate, Dataverse) and locally-run AI tooling (Claude). Provide solution architecture, prompt engineering, integrations (Graph API/REST), compliance with Azure/Entra/Purview/DLP, mentoring, testing, and adoption support within regulated environments.

Design, develop, and deploy production-ready AI systems (deep learning, NLP, computer vision). Build APIs and microservices, integrate models into software stacks, optimize inference performance, maintain and retrain models, and research generative AI and advanced ML techniques.

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.

Design, build, deploy, and operate production-grade AI agents and multi-agent systems. Implement RAG, agent orchestration, copilots, observability, memory patterns, and enterprise integrations while ensuring governance, reliability, and onshore/offshore delivery.

Join a talent network to build full-stack, AI-native products and platforms. Design and implement LLM workflows, agentic systems, RAG/GraphRAG, vector DBs, frontends (React/Next.js), backends (Python/Node.js/FastAPI/Django), integrations, asynchronous workflows, and production infrastructure while working closely with founders and product teams to convert ambiguous problems into scalable solutions.

No responsibilities or details were provided. The posting only lists the job title "AI Engineer" with no additional information about duties or expectations.

Own and evolve ShiftKey's AI infrastructure: production retrieval/indexing, agentic platforms on AWS, graph-based multi-hop retrieval design, platform APIs, reference agents, and cost/observability guardrails for regulated (HIPAA) workflows.

This role involves developing and optimizing LLM applications, architecting workflows, and integrating AI functionalities. Strong experience in AI/ML and collaboration is required.

The role involves building and scaling LLM-powered applications, managing the full product lifecycle, ensuring system reliability, and collaborating across teams to deliver impactful AI-driven features.

Design and deliver AI-enabled features for a web-based government workflow application using LLMs, RAG, and agentic patterns. Build POCs and production-ready systems, implement orchestration frameworks and prompt engineering, integrate AI/LLM APIs, maintain full-stack apps, establish observability and guardrails, and ensure security and CI/CD practices.

Own and administer Salesforce and NetSuite while designing and deploying production-grade AI-driven automations and agent workflows. Ensure data governance, PHI/HIPAA compliance, release management, and quote-to-cash integrity. Partner cross-functionally to prioritize work, coach teams, document systems, and measure impact of automation on operations and revenue.

Lead implementation of AI strategy and integrate generative AI/LLM capabilities into client applications. Build and operate cloud-native and on-prem model infrastructure, deliver POCs/MVPs with cross-functional teams, optimize LLM cost/token efficiency, and ensure responsible AI, security, and compliance across commercial and public sector environments.

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.

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.

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.

Develop AI agents leveraging large language models, integrate external tools, and ensure production-ready AI features while collaborating across teams.

Design and build LLM-powered agents for hardware product development. Responsibilities include ensuring effective coordination among engineering disciplines and integrating AI systems with existing tools.

The AI Engineer will initially function as a Full-Stack Engineer, progressing to integrate AI solutions into products, enhance efficiency, and shape AI application at Sharebite.

Design and implement a self-learning system for autonomous trading agents, automate strategy generation, and build model hosting infrastructure.

Build and ship AI-native systems for a fintech app: AI agents, RAG/retrieval systems, tool-calling and automation for onboarding, KYC, support, risk, CRM and operations. Work with product and engineering to design reliable, low-hallucination, cost- and latency-aware solutions and turn manual processes into scalable AI-enabled systems.

Design, build, and operate internal agentic workflows on OpenRouter to automate support or GTM operations. Integrate with ticketing/CRM systems, ensure reliability via evals, guardrails, and monitoring, measure automation impact, and iterate with support, GTM, and engineering teams.

Lead AI-assisted developer experience across TypeScript/Node and Python codebases. Evaluate and govern AI dev tools, optimize developer workflows (builds, linting, tests), own devcontainer strategy, improve test infra, maintain shared libraries and CI/CD, and drive developer onboarding, observability, and platform adoption.

The Senior AI Engineer will develop AI-powered features, mentor engineers, and integrate large language models to enhance user experiences and ensure product reliability.