Top Natural Language Processing (NLP) Jobs in Chicago, IL

Design, train, and implement domain-specific LLMs for automation in restoration companies. Collaborate with teams, coordinate AI engineering, and ensure model reliability.

Lead the design and delivery of scalable voice and chat solutions using Amazon Connect, focusing on integrations, reliability, and performance optimization.

Lead design, development, and productionization of ML and LLM/NLP systems. Own feature engineering, model evaluation, monitoring, and bias analysis. Partner with Product and Engineering to drive operational impact and act as senior technical owner and reviewer for data science work.

The Staff AI Software Engineer will own the full AI systems lifecycle, from model development to production deployment, focusing on NLP and generative AI solutions, while collaborating with cross-functional teams to optimize and drive solutions.

The role involves researching and developing large language models (LLMs) with a focus on transformer architecture, data curation, distributed training, and optimization. Responsibilities include conducting experiments, collaborating with teams, and staying updated on deep learning advancements.

Own and evolve revenue calibration models tying CX interactions to revenue, design causal inference and experimentation frameworks, build LLM-powered classification/NLP pipelines, productionize models with Analytics Engineers, define segmentation and behavioral signals, and ensure statistical rigor for executive reporting and regulatory defensibility.

Lead end-to-end product development for an AI agent and platform that automates customer interactions. Define roadmap, prompt strategies, evaluation frameworks, and guardrails. Partner with engineering and OpenAI to deploy models and voice/workflow automation, drive experiments to improve automation rates, quality, and customer satisfaction, and establish AI best practices.

Lead end-to-end development of Jerry.ai's AI platform, building and scaling LLM-driven agents and integrations. Define prompt strategies, evaluation frameworks, guardrails, and metrics to improve automation, quality, and customer satisfaction. Act as technical translator across teams and partner with OpenAI to deploy new model and voice capabilities.

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.

Design and build AI agent systems, focusing on LLM applications and autonomous workflows. Requires expertise in AI integration and Python development.