Top Remote Artificial Intelligence Engineer Jobs in Chicago, IL

Build and deploy agentic AI systems that automate customer experience operations, including reporting, QA analysis, case workflows, SLA risk detection, and knowledge maintenance. Develop orchestration layers, integrations, memory architecture, retrieval pipelines, and escalation logic. Measure AI reliability and business outcomes while partnering with data, QA, workforce management, and customer support teams. Productize successful tools for organization-wide scale.

Design, build, and launch AI-focused software capabilities using Python and distributed-systems principles. Own initiatives from solutioning and architecture through production implementation and launch while remaining hands-on with code. Collaborate with Nextiva engineering and product teams, Google Gemini, and other external technology partners to extend AI capabilities across partner environments and the Nextiva ecosystem. Solve ambiguous early-stage technical problems and contribute to system design and architecture decisions.

Build and ship production-grade AI features by embedding with clinical, product, and operations teams. Own solutions end to end, including architecture, evaluation, validation, governance, deployment, monitoring, and human-in-the-loop safeguards. Develop agentic capabilities using tool calling, RAG, MCP integrations, and orchestration on AWS Bedrock. Establish acceptance criteria and risk controls, contribute reusable platform assets, and improve AI fluency across teams. The role requires strong Python, cloud engineering, full-stack development, and experience delivering validated AI features in production.

Design, build, deploy, monitor, and optimize production machine learning and LLM systems, including RAG pipelines, agentic workflows, fine-tuned models, and embeddings. The role owns technical delivery from architecture through iteration, collaborates with enterprise clients, mentors junior ML engineers, conducts code reviews, and helps establish internal engineering standards and tooling.

Lead design and deliver SAP BTP AI solutions for Finance. Partner with SAP Finance teams to identify AI use cases, build GenAI capabilities (RAG, Joule Skills, agents), ensure secure, scalable integrations, validate feasibility via POCs, and produce production-ready solutions for reconciliations, controls, reporting, and financial close.

Build and deploy production-grade agentic AI workflows for Fan Ops and Marketplace Ops, including issue triage, response drafting, dispute resolution, risk scoring, and SLA monitoring. Integrate internal and external data systems, create usable interfaces and retrieval tools, coach nontechnical operators, and own deployment, monitoring, iteration, and impact measurement. The role also evaluates emerging LLM tools and establishes AI-embedded engineering practices across Operations.

Lead design, build, and deploy of large-scale data platforms for LLMs, RAG, and agentic AI systems. Hands-on coding, architecting fault-tolerant pipelines, establishing MLOps/DataOps best practices, mentoring engineers, and operationalizing research into production across Exabyte-scale distributed systems.

Owns the strategy, architecture, and implementation of production AI and machine learning systems for personalization, search ranking, relevance, and patient-provider matching. Builds recommendation engines, retrieval models, LLM applications, and scalable ML infrastructure while establishing AI safety, evaluation, and MLOps standards. The role requires hands-on model development, technical leadership, mentoring, and responsible deployment of AI in a regulated mental healthcare environment.

Unpaid AI engineering internship to design, build, and deploy ML solutions (NLP, computer vision, recommenders). Work on data preprocessing, model training/fine-tuning, containerized FastAPI microservices, and scalable deployments on Google Cloud Run. Contribute to code reviews, GitHub repos, and integration of third-party AI APIs.

Design, build, deploy, monitor, and optimize production ML and LLM systems, including RAG pipelines, agentic workflows, fine-tuning, and embeddings. Own technical delivery from architecture through iteration, collaborate with client engineering and product teams, mentor junior ML engineers, and help establish internal technical standards and tooling.

The Technical Lead will design, build, and modernize IVR and Voice Bot platforms, providing technical ownership and leadership in a hands-on role within the Contact Center Technology department.

Lead the architecture and long-term roadmap for Relativity’s machine learning platform. Design scalable training, inference, monitoring, and ML operations systems; evaluate emerging technologies; improve model performance and cost efficiency through optimization techniques; and ensure secure, reliable AI services. Partner with product, program, and data science leaders, guide cross-functional initiatives, establish engineering standards, and mentor senior engineers across multiple teams.

Lead the architecture, development, and scaling of data pipelines and infrastructure for a customer-facing Data Lake platform. Build ETL and ingestion systems using GCP, Debezium, Pub/Sub, Dataflow, BigQuery, Python, Golang, and SQL. Establish engineering standards, deliver strategic projects, mentor engineers, influence cross-functional technical decisions, optimize data services, and shape the platform roadmap.

Build and deploy production AI agents for real customers. Responsibilities include orchestrating LLM pipelines, integrating APIs and tools, developing React interfaces, implementing RAG and document-processing systems, deploying serverless applications, and monitoring performance. The role requires strong Python or JavaScript skills, hands-on OpenAI and multimodal LLM experience, vector databases, agent frameworks, function calling, and full-stack integration. Applicants must be pursuing a Master's or PhD in a technical field.

The Staff AI Engineer will analyze AI quality, implement improvements, manage projects, collaborate with teams, and shape AI capabilities in a remote-first environment.

Build and operationalize production AI solutions for federal clients, from stakeholder discovery and architecture through evaluation, deployment, governance, monitoring, and optimization. Develop LLM applications using retrieval, structured outputs, tool use, and agentic workflows. Define quality metrics, create evaluation sets, document limitations, advise clients on AI use cases, and mentor engineers while contributing to reusable internal patterns and products.

Owns AI capabilities end to end, from stakeholder discovery and technical design through development, evaluation, deployment, monitoring, and documentation. Builds LLM applications using retrieval, structured extraction, tool use, and agent patterns; defines measurable quality criteria; balances accuracy, cost, latency, security, and complexity; supports client delivery and proofs of concept; and mentors engineers new to AI.

Build production-quality AI applications for federal clients and internal products. Responsibilities include gathering requirements, designing prompts and retrieval workflows, integrating APIs and data, creating evaluations, investigating failures, documenting limitations, and supporting proofs of concept and client demonstrations. The role requires client-facing communication, comfort with ambiguity, and the ability to translate business needs into working software.

Build production-ready agentic AI applications that solve customer problems. Design workflows, plugins, and tool integrations; translate ambiguous needs into intuitive AI experiences; rapidly iterate applications into production; integrate APIs and enterprise systems; and partner with platform and product engineers. Own customer-facing quality, including user experience, reliability, performance, and evaluation, while identifying reusable patterns across deployments.

Lead Affirm’s enterprise AI security review program, threat model LLM and agentic systems, review code and configurations, and develop security guardrails, tooling, policies, and monitoring. Evaluate AI-enabled SaaS vendors, identify emerging vulnerabilities, support incident response, and lead cross-functional initiatives across Security, Engineering, Legal, Privacy, and Compliance. The role requires expertise in AI security architecture, threat modeling, IAM, cloud infrastructure, policy-as-code, and regulated enterprise environments.

Build and operate scalable data pipelines, evaluation frameworks, and production AI/ML/NLP models. Own the full lifecycle from data collection and transformation through training, deployment, monitoring, and continuous improvement. Develop robust tooling, observability, and quality checks while collaborating with product and engineering to integrate AI capabilities into user-facing features. Work pragmatically in a fast-moving startup environment and translate business needs into effective data and machine learning solutions.

Designs, builds, and deploys enterprise AI solutions for pharmaceutical and healthcare marketing operations. Leads client discovery, translates business needs into scalable architectures, develops integrations and AI workflows, and delivers production-ready cloud solutions. Responsibilities include generative AI, RAG, data pipelines, enterprise systems integration, authentication, observability, CI/CD, Docker, Kubernetes, and Google Cloud Platform. Serves as a technical advisor while collaborating with product, engineering, data, security, and delivery teams.

Develop and deploy scalable AI and machine learning applications, including forecasting, anomaly detection, and natural language processing solutions. Design software architectures, troubleshoot full-stack issues, participate in technical design sessions, and collaborate with cross-functional teams. The role requires production machine learning experience, strong programming skills, and the ability to learn evolving AI technologies. Preferred experience includes web service frameworks, distributed systems, and Azure cloud computing.

Build foundational systems for AI models across data modalities, develop agentic systems and specialized models, and implement reinforcement learning and active inference solutions. The role involves hands-on architecture and product development, requiring Python backend expertise, cloud deployment knowledge, DevOps practices, and the ability to contribute across the technical stack in an early-stage startup.

Create reinforcement learning environments to evaluate AI systems solving complex software engineering tasks with MCP tools. Responsibilities include designing reproducible environments, deterministic verification, golden reference solutions, bug fixes, feature implementation, codebase refactoring, and performance optimization across software codebases.