Top Generative AI Jobs in Chicago, IL

In this role, you will design and deploy advanced machine learning and Generative AI solutions, leading projects from conception to deployment while mentoring junior team members.

Designs, develops, and deploys enterprise Generative AI platforms using LLMs, RAG, agentic architectures, and modern orchestration frameworks. Builds Python microservices and AI workflows across Azure and AWS, while implementing GenAIOps, containerization, infrastructure as code, CI/CD, monitoring, security, governance, and compliance. Partners with engineering, platform, architecture, security, and product teams to deliver scalable, resilient, production-ready AI solutions.

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.

Leads Slate’s GenAI strategy, architecture, and production deployment across vehicle engineering, manufacturing, supply chain, software, and go-to-market operations. Owns enterprise-scale GenAI platforms, agentic systems, data and context layers, model serving, evaluation, guardrails, and observability. Applies physical AI to robotics, computer vision, predictive maintenance, and factory feedback loops. Recruits and develops a high-performing technical team while remaining hands-on with coding, architecture, stakeholder engagement, and technical standards.

Lead research and development of generative AI and recommendation solutions for Netflix promotional assets and content discovery. Responsibilities include tracking LLM research, developing summarization and LLM-as-judge systems, training and evaluating models, optimizing generated content for audience impact, partnering with engineering teams, defining ML roadmaps, and communicating technical strategies to stakeholders.

Design, develop, deploy, and optimize Generative AI applications using LLMs and AI frameworks. Build scalable APIs, automate workflows, integrate foundation models with enterprise systems, and improve model performance through testing and experimentation. Implement monitoring, quality assurance, governance, security, compliance, documentation, and risk mitigation. Evaluate emerging technologies, support proofs of concept, and collaborate with product, engineering, and business teams.

Leads the architecture, development, deployment, governance, and optimization of enterprise Generative AI solutions. Builds applications using LLMs and foundation models, develops prompt engineering frameworks, supports AI strategy, ensures secure and responsible deployment, mentors engineers, evaluates emerging technologies, and translates business needs into scalable technical architectures.

Deploys, integrates, and scales enterprise generative AI solutions within customer engineering teams. Builds intelligent agents and multi-agent systems using Vertex AI, ADK, and MCP; connects AI systems to enterprise databases; develops real-time data pipelines; and operationalizes LLMs across cloud environments. The role also includes model training, tuning, evaluation, deployment, vector-based grounding, cloud infrastructure integration, stakeholder communication, and translating business goals into technical architectures.

Own and improve the AI Social render pipeline and agent loops that generate on-brand Instagram carousels and reels. Build prompts, structured outputs, tool-calling, and media composition to ship high-quality generative image, video, voice, and music products in collaboration with product, design, and content strategy.

Design and deploy production-ready LLM pipelines and agentic workflows, automate large-scale multimodal dataset pipelines, monitor and improve agent performance, and integrate AI agents with 3D scene composition. The role also involves process documentation, incident response, and continuous improvement, with collaboration across the engine team and company founders.

Partners with customers and internal teams to design, deploy, and optimize generative AI and LLM inference solutions. Develops proofs of concept, supports NVIDIA technology adoption, analyzes AI workload performance and power efficiency on Kubernetes, and advises developers, researchers, and data scientists. The role requires expertise in deep learning frameworks, GPU orchestration, MLOps, containerization, observability, and LLM inference, with occasional travel to conferences and customers.

Reviews and evaluates AI-generated text, translations, audio, images, video, and other data; identifies errors and quality issues; annotates and labels content; provides written feedback; follows detailed project guidelines; and completes independent assignments within deadlines.

Design, build, and deploy production-ready intelligent solutions using machine learning, deep learning, generative AI, and large language models. Develop RAG pipelines, integrate AI services, manage model deployment and MLOps workflows, and apply strong software engineering practices. Collaborate with engineering, product, and data teams in a distributed environment to create scalable, reliable, responsible AI-powered products.

Build and deploy AI-powered applications for customers, combining Python and TypeScript application engineering with LLMs, generative AI, agent orchestration, APIs, distributed systems, and cloud platforms. Partner with stakeholders to understand requirements, design scalable architectures, rapidly prototype solutions, integrate AI capabilities, and evolve proofs of concept into production systems. The role also involves customer-facing consulting, technical decision-making, and collaboration throughout the solution lifecycle.

Deploys, integrates, and scales enterprise generative AI solutions in customer cloud environments. Responsibilities include architecting GCP and Vertex AI infrastructure, deploying and optimizing LLMs, building RAG data and vector search pipelines, managing GPU/TPU workloads on GKE, automating infrastructure with Terraform, and advising clients on AI safety, prompt engineering, hallucinations, and inference costs. The role also collaborates with research and platform teams and requires travel to client sites.

Design and execute ML research and end-to-end model development for generative and summary-generation systems. Lead rigorous experiments, translate findings for stakeholders, partner with engineers to deploy models, track state-of-the-art advances, and define roadmaps to improve promotional writing and discovery for members.

Embed with client engineering teams to operationalize LLMs and retrieval systems across multi-cloud environments. Build agentic solutions using Vertex AI Agent Builder and ADK, connect agents securely to BigQuery/Spanner via MCP, implement streaming pipelines (Dataflow) for real-time inference, and manage model training, tuning, deployment, and evaluation on Vertex AI. Provide consultative guidance, cross-functional collaboration, and clear stakeholder communication to ensure business value from Generative AI solutions.

Integrate LLMs, RAG, and agentic-AI into secure decision-support software. Design prompts and inference workflows, connect AI to backend services, evaluate outputs, build tests, document designs, and collaborate with engineering, security, and DevSecOps teams to deliver reliable mission-capable AI features.

Build and deliver enterprise Generative AI platform services, APIs, microservices, and reusable frameworks. Implement CI/CD, automated tests, model deployment, inference pipelines, observability, and scalable secure solutions while collaborating with architects, data scientists, and stakeholders.

Senior technical leader responsible for architecting and operationalizing generative AI across the enterprise. Drive end-to-end GenAI architecture, select models and agent frameworks, productionize RAG and agent solutions, ensure observability, guardrails, and cost optimization, integrate with AWS and DevOps toolchains, advise CXO stakeholders, shape AI strategy, and define governance and upskilling programs.

Lead architecture and delivery of Generative AI solutions across Life & Annuities insurance, defining secure, scalable patterns (RAG, vectors, agents), integrating AI with policy administration, underwriting, and third-party systems, and enforcing governance, observability, and compliance while mentoring engineering teams and guiding cloud and data platform choices.

Evaluate, annotate, and quality-check AI-generated text, image, audio, and video content in Spanish. Identify edge cases, ensure guideline compliance, participate in calibration, and provide feedback to improve datasets and annotation workflows for generative AI models.

As a Senior Solutions Architect, you will assist customers in building solutions with NVIDIA's AI technology, focusing on Generative AI and Large Language Models while collaborating across teams for performance analysis.

Own generative AI discovery, scaling, and maintenance for a portfolio of AI characters. Select and standardize models, build pipelines for face consistency and video, train and debug LoRAs, control GPU costs, design prompt architectures, define evaluation metrics and quality gates, and translate creative briefs into technical specs while testing frontier models and tooling to scale the studio.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.