Top AI Jobs in Chicago, IL

Develop and maintain population-scale NLP and LLM pipelines that extract and validate suicide-related phenotypes from VA clinical notes. Prepare analysis-ready datasets for statistical genetics research, ensure reproducibility and compliance with PHI/PII security requirements, troubleshoot performance and data quality issues, and provide technical documentation and milestone updates to the research team.

Leads an AI Evaluation Engineering team responsible for validating generative AI products, including intelligent agents and digital assistants. Provides technical direction, manages team performance and development, owns product quality, and establishes engineering best practices. The role requires expertise in GenAI architecture, evaluation methodologies, observability, automated testing, cloud and edge environments, reliability, safety, RAG assessment, and human-in-the-loop validation.

Owns the vision, roadmap, governance, lifecycle, and adoption of AI/ML technology platforms. Translates stakeholder needs into platform requirements, prioritizes capabilities, coordinates engineering dependencies, and improves reliability, security, cost, and developer experience. Partners with engineering and product teams on integrations, standards, agile delivery, incident communications, operational improvements, and value measurement. Requires extensive platform or technical leadership experience, AI/ML expertise, Azure, and Databricks experience.

Develop and validate clinical heuristics and algorithms using real-world healthcare data. Build Python profiling pipelines, automated data-quality checks, and longitudinal patient cohorts. Analyze complex clinical databases and ETL pipelines, evaluate AI, NLP, and heuristic outputs, and collaborate with clinical, labeling, engineering, and data science teams. Maintain documentation, configuration, metrics, and validation standards for transparent, high-quality clinical analytics.

Leads Salesforce’s Forward Deployed Engineering team in delivering enterprise Agentic AI solutions. Defines deployment strategy, multi-agent architectures, development lifecycle standards, governance, and operating models. Builds and develops a high-performing technical team, partners with Product, Engineering, Sales, Security, and Legal, and influences Agentforce’s product roadmap. Requires deep expertise in enterprise AI, LLMs, RAG, distributed systems, orchestration, and secure production deployments, with 30% customer travel.

Build and deploy governed AI agents, automations, and multi-agent systems for business teams. Responsibilities include identifying AI opportunities, translating needs into requirements, prototyping, integrating enterprise systems, instrumenting telemetry, measuring ROI, monitoring risks and costs, and applying governance controls. The role also develops reusable patterns, documents solutions, enables business users, and communicates lessons learned to the AI Center of Excellence.

Designs, builds, and operates scalable AI platforms across multi-cloud and hybrid environments. Responsibilities include developing GenAI capabilities, RAG and agentic AI systems, observability, evaluations, guardrails, security, governance, and deployment automation. The role leads platform architecture, prototypes emerging technologies, ensures performance and reliability, partners with cross-functional teams, conducts design reviews, and mentors engineers.

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

Build and deploy generative AI solutions, including APIs, MCP servers, AI agents, LLM integrations, and full-stack applications. Develop scalable AWS cloud services, observability systems, evaluation pipelines, and production agent workflows. Collaborate with Product, UX, Security, Legal, and engineering teams to design interfaces, adapt AI tools to company codebases, and support customer experience and developer productivity use cases.

Research, design, train, and deploy multimodal AI models using clinical and molecular data to support precision medicine, biomarker discovery, therapy outcome prediction, and companion diagnostics. The role requires collaboration with life sciences companies, regulatory alignment for healthcare products, and publication of original research. Responsibilities include working with pathology slides, genomic profiles, spatial omics, and longitudinal patient records while contributing hands-on model development and product-level engineering.

Leads health economics and outcomes research and real-world evidence projects for pharmaceutical, biotech, academic, and other partners. Designs and analyzes pharmacoepidemiologic studies, treatment patterns, healthcare utilization, causal inference, and survival analyses. Builds reusable analytical infrastructure, collaborates with scientists, engineers, and product teams, interprets results for stakeholders, supports publications and regulatory work, and ensures compliance with data governance and applicable regulations.

Develop and operate autonomous AI agents and enterprise integrations using Databricks, Claude, MCP, and event-driven architectures. Build Databricks Apps, maintain MCP connectors, monitor agent performance, manage incidents, and document workflows, triggers, dependencies, and data sources. Partner with business, governance, and process teams to identify suitable agentic AI applications, particularly for go-to-market systems, while applying logging, auditing, naming, and change-management standards.

Leads the delivery, integration, adoption, and scaling of AI-powered solutions across the firm. Translates AI strategy into production workflows, automation systems, enterprise integrations, and measurable business outcomes. Oversees technical implementation, vendors, delivery roadmaps, governance, change management, training, and continuous improvement. Partners with technology, data, and business teams to embed AI into workflows and improve productivity, client experience, and operational efficiency.

Build, test, deploy, and evaluate machine learning models using established frameworks. Support feature engineering, data preprocessing, automated training and monitoring pipelines, and production AI integration. Collaborate with engineering and product teams to define AI requirements, research performance improvements, and respond to critical IT incidents. The role requires advanced quantitative education, 5+ years of AI/ML engineering experience, Python expertise, and knowledge of deep learning and generative AI.

Build and maintain production LLM-based agents and automated digital customer success journeys. Analyze customer behavior, design playbooks and agent architectures, write Python integrations, create evaluation and regression tests, deploy and monitor production systems, and continuously improve performance. The role partners cross-functionally to increase product adoption, reduce churn risk, and improve retention and expansion across a high-volume B2B SaaS customer base.

Leads Samsara’s GTM AI Engineering team, building production AI agents, copilots, workflow automations, predictive models, and evaluation systems. Owns the shared AI platform, architecture, governance, observability, model routing, cost controls, and CRM intelligence. Partners with Sales, Customer Success, Support, Product, and Engineering to drive adoption and measurable business impact. This hands-on director role involves coding and prototyping approximately 70% of the time while hiring, developing, and guiding the engineering team.

Leads OuterBox’s internal AI strategy, AI engineering team, and development of client-facing AI services. Identifies market opportunities, converts internal capabilities into priced consulting offerings, oversees production deployments and partner programs, and drives AI adoption, training, and measurement. Partners with executives, clients, engineering, and technology teams on architecture, security, cloud governance, reliability, pricing, and ROI.

Leads Boomi’s enterprise AI strategy, agentic AI transformation, and AI data foundation. The role oversees multi-agent architecture, model strategy, AI gateways, knowledge graphs, semantic layers, retrieval, trust and governance systems, evaluation, observability, and production data pipelines. It manages and mentors distributed AI and data engineering teams, drives enterprise adoption, defines architecture and operating standards, and communicates strategy to executives, customers, and partners.

Tavern Research welcomes expressions of interest from curious, adaptable candidates interested in building technology at the intersection of data science, politics, and media production. Applicants should be comfortable solving difficult problems, learning quickly, using emerging tools, and performing under pressure. Political experience is helpful but not required. Employees are expected to work regularly in person from the company’s Chicago office.

Provide bilingual Somali-English support to learners in a virtual data annotation program. Facilitate labeling practice, answer questions, review and grade multimodal annotation assignments, conduct office hours, create walkthroughs, monitor learner progress, provide feedback, and escalate attendance or performance concerns. TAs must understand annotation guidelines, AI output evaluation, quality assurance, secure data handling, and AI training fundamentals.

Own the enterprise AI strategy, roadmap, governance, investment priorities, and measurement framework. Identify and prototype high-value AI use cases, guide pilots into production, establish responsible-AI standards for sensitive data, and communicate outcomes to executives. Build adoption through enablement and reusable practices while coordinating technology, business, legal, security, data, and vendor partners. Lead the emerging AI strategy function and hold teams accountable for delivery and value realization.

Leads Year Up United’s AI innovation portfolio, maintaining visibility into initiatives, managing intake and governance, running low-cost pilots, and making evidence-based go/kill decisions. The role evaluates emerging AI capabilities, builds organizational AI fluency through communities and Champions, partners cross-functionally, and hands validated pilots to technical teams for scaling. It also includes mentoring students and occasional travel to organizational sites.

Build and operate Affirm’s AI platform, including LLM and MCP gateways, agent runtimes, evaluation services, and governance controls. Design highly available backend systems, manage identity, quotas, routing, caching, reliability, observability, and security for AI workloads. Own projects from design through launch and on-call, establish engineering standards, collaborate across teams, mentor engineers, and help scale AI systems into production.

Design, develop, test, operate, and maintain AI-enabled software and infrastructure. Build production-ready code, RAG pipelines, vector search integrations, and agentic AI components. Follow SRE/DevOps and responsible-AI practices, participate in code reviews, troubleshoot model quality, and collaborate across engineering, product, and business teams to deliver scalable GenAI solutions.

Leads enterprise AI assurance and audit engagements covering Generative AI, LLMs, AI governance, Responsible AI, risk management, and AI-enabled processes. Evaluates controls for bias, fairness, transparency, privacy, hallucinations, misuse, human oversight, monitoring, and regulatory compliance. Advises senior management and the Board, identifies control gaps and emerging risks, develops audit plans, manages teams and resources, and delivers objective recommendations across the organization.