Top AI Engineer Jobs in Chicago, IL

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

Lead AI architecture and delivery, design and deploy production AI systems (LLMs, RAG, embeddings, vector DBs, agents), build document intelligence for English/Arabic finance documents, create internal AI assistants, define evaluation/monitoring/security, and collaborate with product, risk, compliance, and operations.

Design, develop, and deploy production AI systems (deep learning, NLP, CV). Build APIs, integrate models into products, optimize inference (quantization, pruning, ONNX), maintain model lifecycle, and research generative AI, reinforcement learning, and advanced techniques.

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.

Design, build, and ship agent-first AI products end-to-end. Implement production-ready agents using frontier models, ensure observability and quality via model-based evals and agent tracing, drive product definition with PMs/designers, and share agentic engineering practices to accelerate the team.

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

As a Staff AI Engineer, you will drive product-centric development of AI technologies, enhance evaluation systems, optimize reasoning strategies, and lead production-grade model tuning while balancing performance and cost.

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 Senior GTM Engineer in AI Innovation will strategize and implement AI initiatives across Marketing, Sales, and Customer Success while also leading cross-functional efforts and managing a growing team.

Build and own conversational voice AI products end-to-end: architect, implement, and deploy the platform. Design infrastructure, develop front-end and back-end systems, deliver reliable customer outcomes, and collaborate directly with the CTO to scale the product for enterprise customers.

Lead design, development, and deployment of generative AI/ML solutions (LLMs, RAG, agentic frameworks) for VBC and RCM. Build scalable pipelines with Python/PySpark/T-SQL, integrate Snowflake and GCP, apply MLOps (MLflow, Docker, CI/CD), ensure LLM security and Responsible AI, and collaborate across teams to deliver measurable automation and production-grade models.

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.

As a Staff NLP Scientist, you will solve complex medical document understanding problems using NLP and Computer Vision, optimizing models for performance and scalability.

The NLP Scientist will solve complex medical document understanding problems using NLP and CV methodologies, optimize models for performance, and make data-driven decisions.

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.

Lead architecture and delivery of high-throughput transactional services and agentic AI workflows. Build production Python and Go microservices on AWS, design observability and CI/CD, resolve distributed-system performance issues, run technical reviews and ADRs, mentor engineers, and contribute to on-call and incident response.

Design, build, and operate production multi-agent AI systems and automation for Marketing Operations. Integrate LLMs with business data (BigQuery, CRMs, Slack), build MCP servers, APIs, and scalable serverless/containerized services, implement observability and governance, and partner with GTM teams to deliver measurable automation and self-service workflows.

Lead development of AI and ML solutions for healthcare systems, overseeing teams, driving use case development, and ensuring operational excellence while managing global delivery operations.

Design, build, and deploy production-ready AI solutions: data pipelines, dataset preparation, model training/tuning (including LLMs, embeddings, RAG), model optimization, and integration into enterprise systems while ensuring data security, governance, and collaboration with stakeholders.

Support development and evaluation of AI features and RAG pipelines: prompt experimentation, data preparation, embedding generation, model evaluation, debugging, and basic AI service development using Python and ML tools under senior engineer guidance.

Lead design, build, deployment, and maintenance of production-grade GenAI systems (LLM apps, agentic workflows, RAG, retrieval, and evaluation). Architect scalable ML services, implement model serving and observability, optimize latency and cost, fine-tune LLMs, translate prototypes to production, mentor engineers, and produce runbooks and documentation.

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.

Lead design and production of AI/ML solutions on the data platform (LLMs, RAG, embeddings). Build AI infrastructure (feature stores, vector DBs, model pipelines), drive AI-enabled data products, establish responsible AI best practices, and mentor engineers to integrate AI across analytics and product workflows.

Design, build, and evaluate AI systems, focusing on natural language understanding and automation. Collaborate with teams to ensure production quality and performance metrics.

Design and deliver agentic systems for enterprises, integrating tools and data while ensuring safety and compliance. Collaborate with clients to optimize business workflows and system architectures.