Top AI Engineer Jobs in Chicago, IL

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.

Own quality control, regulatory alignment, and performance reporting for AI-powered clinical extractors. Analyze accuracy, consistency, data quality, provenance, and performance drift; guide extraction logic and clinical interpretation; support FHIR and CSV data exports; and contribute to governance, de-identification, validation, and client reporting. Collaborate with Data Science, Engineering, and Clinical Informatics while serving as a clinical subject-matter expert, particularly in oncology and real-world evidence.

Lead end-to-end client AI transformation engagements: discover and frame executive problems, design and build production-credible PoAs (data pipelines, integrations, LLM/RAG apps) within 2–3 weeks, present to executives, hand off to delivery, mentor junior FDEs, and convert PoAs into full engagements.

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, maintain, and improve production AI services for document understanding including LLM pipelines, background jobs, databases, and internal tools. Implement features, debug production issues, write tests, and contribute to evaluation frameworks to improve model quality and system reliability. Collaborate with a small engineering team and take increasing ownership over platform components.

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.

Project-focused AI engineering internship embedded on a DevOps team. The intern will prototype generative assistants for alert response, build retrieval-augmented systems over permissioned internal data, design guarded agentic remediation workflows, and support internal engineering Q&A. Responsibilities include tool-use workflows, observability integration, evaluation, documentation, Git-based delivery, and strict controls for permissioning, auditing, logging, safety, and cost.

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.

Develop and operate production AI framework components, runtimes, benchmarking systems, performance tools, and integrations. Benchmark and optimize large language model training and inference across GPUs and Microsoft hardware, build automation and observability, investigate cross-stack performance and reliability issues, and deliver scalable platform improvements. Principal engineers additionally set technical direction, lead multi-team initiatives, define architectures, and mentor technical leaders.

Designs, develops, deploys, and maintains secure AI-agent and automation solutions for enterprise and client operations. Builds LLM workflows, backend integrations, AWS serverless infrastructure, event-driven pipelines, database connections, monitoring, and deployment systems. Translates ambiguous business processes into reliable, validated, auditable solutions with human approval controls. Participates in stakeholder discovery, technical documentation, testing, deployment, incident resolution, and continuous improvement.

Leads architecture and hands-on development for AI frameworks, performance systems, benchmarking, and developer tools. Investigates issues across models, compilers, runtimes, services, and accelerator hardware; improves model onboarding, runtime performance, reliability, hardware utilization, and Azure efficiency. Establishes scalable measurement and observability capabilities, influences cross-team technical strategy, delivers production solutions, and mentors engineers while advancing engineering quality and maintainability.

Designs, builds, and operationalizes production-grade generative AI and machine learning systems across AWS and GCP. Responsibilities include LLM applications, agentic workflows, RAG architectures, vector search, model lifecycle management, cloud-native microservices, responsible AI governance, HIPAA compliance, and optimization. The role also leads architecture, collaborates with product, security, data, and compliance teams, mentors engineers, and manages team performance.

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.

Build AI systems for legal teams by designing legal datasets, annotating training and benchmarking data, defining model requirements, translating legal workflows into structured processes, validating product capabilities, engaging clients, and prototyping with AI tools and scripting. Requires a JD and active or prior bar membership, 2–8+ years of legal practice, strong familiarity with litigation, investigations, compliance, or e-discovery workflows, and comfort collaborating with engineers and product teams.

Build and ship production AI capabilities using AWS Bedrock, including clinical document extraction, summarization, retrieval, decision support, and workflow automation. Develop Python and FastAPI applications, integrations, tests, CI gates, and evaluation systems. Work with healthcare data standards, PHI, AWS, MySQL, and server-rendered front ends. Use and verify AI coding tools while communicating tradeoffs clearly in a remote, Linux-based environment.

Partners with clients to identify AI opportunities, translate ambiguous business needs into technical requirements, and build solutions using Microsoft Power Platform and Copilot Studio. Develops prompts, grounding strategies, integrations, and prototypes involving Copilot and Claude. Supports testing, quality assurance, maintenance, troubleshooting, documentation, user training, governance, and data protection in regulated environments while building stakeholder consensus.

Build and deploy production-scale AI/LLM systems: own ML lifecycle from model development to serving, optimize GPU/Kubernetes deployments, design LLM applications and APIs with monitoring, apply CI/CD and version tracking, collaborate with product teams, and contribute to AI engineering best practices.

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 and build real-time, highly concurrent software and cloud-native systems for an AI-powered emergency communications platform. Integrate and deploy LLMs, optimize voice and audio-processing pipelines, and support speech recognition, synthesis, detection, and filtering. Contribute to infrastructure, observability, security, scalability, and fault tolerance. Provide technical leadership through architecture decisions, code reviews, mentoring, and engineering best practices.

Design, build, deploy, and optimize production ML and LLM systems, including RAG pipelines, agentic workflows, fine-tuning, and chatbot architectures. Own technical delivery from architecture through monitoring and iteration, collaborate directly with enterprise clients, mentor junior ML engineers, and help establish internal technical standards and tooling.

Own and operate the company’s enterprise AI platform across Google Cloud, Claude Enterprise, and Gemini Enterprise. Responsibilities include AI platform operations, CI/CD, infrastructure automation, observability, identity and user lifecycle management, marketplace extensions, internal agents and tooling, AI governance, evaluation, onboarding, documentation, and enablement. The role requires 3–5 years of platform, DevOps, SRE, MLOps, backend, or infrastructure experience and is onsite four days weekly in Chicago.

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.

Operate and improve enterprise AI/ML platforms across Palantir Foundry, AWS Bedrock, and Amazon SageMaker. Responsibilities include deployment, monitoring, observability, automation, governance, model lifecycle management, incident response, root cause analysis, CI/CD, infrastructure as code, and production reliability. The role provides technical leadership and mentorship while partnering with data, infrastructure, security, architecture, product, and engineering teams to support cloud-based AI workloads and Generative AI adoption.

Design, develop, and deploy enterprise AI applications using LLMs, RAG, AI agents, vector search, document processing, and cloud-native technologies. Build scalable APIs, integrations, knowledge platforms, and Kubernetes-based services while contributing to architecture, CI/CD, security, testing, and observability. Collaborate with business and technical stakeholders to solve complex problems, evaluate emerging AI technologies, and mentor team members.

Build and maintain the Python-based backend engine for GitLab Duo Chat, including agentic LangGraph flows, the Flow Registry, and Duo Workflow Service. Integrate chat capabilities with GitLab’s Rails monolith and GraphQL API, develop secure and tested high-scale features, optimize performance, review code, address technical debt, and collaborate cross-functionally. Participate in weekday, weekend, and occasional nighttime Tier 2 or Tier 3 on-call rotations.