Top Remote Artificial Intelligence Engineer Jobs in Chicago, IL

Design, build, test, deploy, monitor, and maintain production LLM-based agentic solutions for Commercial Customer Success. Integrate agents with CRMs and Samsara systems, define evaluation frameworks and success metrics, implement automated tests and monitoring, iterate on models and prompts, and demonstrate impact on customer outcomes and retention.

Designs, develops, and operates enterprise-grade platforms for AI agents and Generative AI applications. Responsibilities include agent orchestration, LLM and AI service integration, secure tool and API invocation, prompt lifecycle management, governance and safety controls, observability, model evaluation, deployment, troubleshooting, and platform optimization. The role collaborates with engineering, security, product, and data teams to deliver scalable, reliable, and compliant AI solutions.

Design, develop, and deploy scalable AI applications and services across the OneStream platform. Build production machine learning solutions, troubleshoot full-stack issues, participate in technical design sessions, and collaborate with cross-functional teams. The role involves applying forecasting, anomaly detection, and natural language processing while maintaining reliable, well-documented code and continuously learning emerging AI technologies.

Own product-layer performance for agentic AI systems in healthcare. Design prompting, retrieval, context, memory, tool use, evaluation, escalation, and human-review workflows. Build production-grounded evaluations, diagnose failures, improve accuracy, reliability, latency, cost, and steerability, and approve model changes. Partner with Product and Engineering while maintaining model cards, intended-use guidance, limitations, and failure-mode documentation for safe, transparent deployment.

Build and modernize enterprise data and AI ecosystems, including real-time streaming pipelines, data products, semantic layers, knowledge graphs, RAG architectures, and GenAI infrastructure. Develop synthetic data workflows, automated data quality systems, and reliable cloud data platforms. Collaborate with architects, DevOps, and stakeholders, mentor engineers, lead prototypes, and apply AI solutions to insurance use cases while ensuring scalability, governance, monitoring, and operational resilience.

Build and deploy AI-powered solutions across Sales, Marketing, and Customer Support. Diagnose workflows, identify constraints, evaluate whether AI is appropriate, and own initiatives from discovery through deployment. Develop LLM applications using prompt engineering, RAG, agentic architectures, orchestration, guardrails, and enterprise integrations. Measure business value through flow metrics, adoption, ROI, and feedback loops while partnering with technical and non-technical stakeholders.

Design, deploy, and operate production clinical AI agents that synthesize patient data, retrieve clinical knowledge, use tools, generate care recommendations, and escalate to clinicians. Own LLM workflow reliability, observability, cost efficiency, evaluation, safety testing, RAG optimization, orchestration, and lifecycle management. Collaborate with clinical, product, and engineering teams to build reliable AI systems for chronic disease care.

Leads architecture, development, and delivery of secure cloud, data, process intelligence, and AI solutions for federal clients. Builds AWS cloud-native services, scalable data platforms, Celonis process-mining solutions, and agentic AI workflows using Amazon Bedrock and Azure OpenAI. Owns technical direction, engineering standards, security, testing, observability, deployment automation, and reusable product packaging. Supports A-123 continuous controls monitoring, agency implementations, demonstrations, and cloud environment deployments while collaborating with AWS and Celonis partners.

Provides technical leadership and architectural oversight for scalable AI-integrated software. Designs agentic AI systems, MCPs, APIs, RAG solutions, microservices, and prompt strategies; decomposes product goals into engineering tasks; reviews human- and AI-generated code; mentors distributed developers; and improves DevOps and CI/CD pipelines with AI-enhanced testing and reviews.

Build and deploy Guidewire’s production AI claims agent, including prompt and context architecture, RAG, tool integrations, human handoffs, evaluation systems, observability, and voice interactions. Improve accuracy, containment, resolution, latency, and reliability through production trace analysis and disciplined experimentation. Collaborate with insurance carriers to shape workflows, balance autonomy and risk, and harden the system for regulated claims data and real-world policyholder interactions.

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 AI-powered workflows, agents, integrations, and internal tools that automate People Operations. Partner with People, Engineering, and IT to modernize onboarding and other employee processes, while implementing secure access controls, human oversight, escalation paths, and production-ready architectures. Prototype with users, iterate quickly, and measure improvements in efficiency, employee experience, and operational consistency.

Leads customer-facing AI advisory engagements, identifying use cases and designing end-to-end AI deployments across model selection, training, inference, data pipelines, and evaluation. Develops proof-of-value prototypes, enables sales and partner teams, presents at conferences, publishes technical content, and advises enterprise customers on AI strategy. Collaborates across engineering, sales, marketing, alliances, and enablement while independently driving research and production initiatives.

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.

Lead the architecture, development, optimization, and operation of production LLM and AI automation systems. Design capabilities including agents, retrieval, code generation, tool use, model routing, evaluation, and observability. Drive platform modernization, scalability, reliability, architecture, and code quality while mentoring engineers and influencing technical practices across teams. Build prototypes, establish engineering standards, conduct reviews, and communicate complex technical concepts through documentation and cross-functional collaboration.

Lead the design and development of production AI systems for enterprise application automation. Responsibilities include building reliable agents and orchestration, extracting specifications from existing systems, modeling application intelligence and retrieval, developing evaluation and CI pipelines, optimizing reliability and scale, using AI coding tools, mentoring engineers, and documenting technical practices.

Build and optimize developer-facing tools, including IDE integrations, CLIs, code-analysis systems, and CI/CD workflows. Integrate transparent, reliable AI capabilities into developer experiences, optimize high-volume production services, and improve availability, latency, and efficiency. Drive engineering best practices through code reviews, automation, documentation, and technical leadership while mentoring junior engineers. The role also requires communicating complex architectures and operating tooling across enterprise deployment environments.

Own the evaluation, selection, optimization, and production deployment of large language models powering legal and medical document workflows. Build EvalOps and monitoring frameworks, benchmark model quality, cost, speed, and resilience, manage model transitions, optimize RAG and orchestration pipelines, and reduce hallucinations. Partner with product and go-to-market stakeholders while independently implementing changes across staging and production.

Build and harden Nava’s shared AI platform for LLM-based products. Responsibilities include reusable context and agent-state management, safe long-running agent execution, sandbox integration, model routing, subagent orchestration, token-cost optimization, tracing, evaluation, testing, and documentation. The role partners with product teams to create maintainable infrastructure that supports production AI features at scale.

Develop and implement AI-driven applications, workflows, and integrations on the ServiceNow platform. Gather requirements, configure and customize ServiceNow modules, integrate machine learning and NLP capabilities, develop scripts, manage CMDB and service mapping work, test solutions, troubleshoot incidents, and support deployments across environments. The role also involves stakeholder collaboration, documentation, production remediation, and applying ServiceNow AI features such as Now Assist, Virtual Agent, Predictive Intelligence, and Performance Analytics.

Engineer AI-powered threat detections, automated response playbooks, and continuous adversary emulation. Apply machine learning and LLMs to security operations, conduct threat hunting and AI red teaming, map coverage to MITRE ATT&CK, and develop detection-as-code through CI/CD. Build production-grade Python integrations, measurable containment capabilities, and safe autonomous defenses with guardrails. Mentor security professionals and collaborate across threat response, cyber defense engineering, and platform teams.

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.

Develop production AI applications using LLMs, RAG architectures, agentic AI, and multi-agent systems. Build Python APIs and backend services, implement vector search and embeddings, and deploy solutions across Azure AI services. Collaborate with architects, product teams, and stakeholders on enterprise AI projects, architecture decisions, best practices, and technical standards. The role requires strong Python expertise, Azure AI experience, modern AI orchestration frameworks, and CI/CD knowledge.

Build and productionize agentic workflows, RAG pipelines, AI services, backend systems, and ML pipelines for sales automation. Integrate Salesforce, quoting engines, data lakes, and internal platforms using scalable, event-driven architectures. Lead 0-to-1 development of tools for account research, lead prioritization, and quoting while ensuring reliability, observability, performance, and resilience across the GTM technology stack.

Build AI-augmented BI dashboards, semantic layers, and AI-ready metadata. Use AI-assisted tools to accelerate SQL and dashboard development, productionize ML outputs into BI, enforce data quality and governance, and present insights to business stakeholders including C-suite.