Top AI Specialist Jobs in Chicago, IL

Freelance AI Specialist responsible for completing and maintaining a shared business knowledge base, improving AI workflows with Claude and Gemini, creating reusable prompts, connecting AI to business tools, documenting processes, training teams, and reporting progress. The role requires careful fact-checking, strong written communication, confidentiality judgment, basic scripting or automation skills, and self-directed project execution.

Designs, builds, and evaluates complete AI systems for Pearson’s K-12 assessment platform. Responsibilities include predictive machine learning, statistical methods, retrieval, knowledge graphs, generative and agentic AI, deterministic services, human review, and system integration. The role develops evaluation datasets and regression suites, analyzes failure modes, supports model adaptation, monitors deployed systems, and collaborates with engineers, psychometricians, learning scientists, and content experts to create transparent, reliable, and auditable AI capabilities.

Design and build Pearson’s knowledge architecture for AI-powered K-12 assessment and learning systems. Responsibilities include developing knowledge graphs, ontologies, schemas, data pipelines, APIs, retrieval and context architectures, and hybrid AI integrations. The role establishes provenance, versioning, traceability, quality measures, and responsible AI practices while partnering with engineers, AI scientists, psychometricians, learning scientists, and content experts. It requires autonomous innovation, rapid prototyping, rigorous evaluation, and translating educational and assessment expertise into computational representations.

Build and maintain AI agents, automations, and data workflows using Copilot Studio, Power Automate, Python, and related tools. Partner with architecture, engineering, construction, BIM, estimating, and business teams to identify needs, validate AI outputs, extract information from documents, and improve processes. Provide demonstrations, training, coaching, documentation, support, and ongoing solution maintenance while applying data security and responsible AI practices.

Develop and validate machine learning models, training pipelines, inference systems, and numerical methods using Python. Debug algorithmic and system-level issues, optimize memory usage and performance, design systematic tests, and build reproducible workflows. Review challenging ML engineering tasks, use coding agents in development, document technical findings, and deliver high-quality work independently within deadlines.

Reconstructs and redesigns data-driven presentations in Microsoft PowerPoint, creating visualizations and analytics reports that communicate complex insights clearly. Reviews and edits content for accuracy, flow, and narrative impact; applies data analysis and statistical reasoning; and refines slides based on feedback. The role supports AI training projects through high-quality presentation and analytical deliverables.

Review and annotate robotic-system video footage by identifying objects, movements, events, visual features, and anomalies. Provide feedback to improve video datasets and annotation consistency, collaborate with AI teams on annotation protocols, document observations, participate in quality reviews, and recommend improvements to video capture and labeling strategies.

Evaluate and improve the safety, security, and robustness of large language models. Responsibilities include designing jailbreak, prompt injection, adversarial, and tool-use abuse tests; developing regression suites and evaluation frameworks; identifying vulnerabilities; translating findings into safety recommendations; and documenting methodologies and results for technical and non-technical stakeholders.

The AI Engineering Specialist will oversee project tasks, manage expectations, mentor junior members, and design scalable AI systems while working with advanced AI models and ML pipelines.

Perform object detection annotation on visual datasets using digital annotation tools. Label and review objects, apply project guidelines, correct inconsistencies, maintain data quality and confidentiality, and meet productivity standards. The role requires accuracy during repetitive tasks, independent remote work, collaboration with project teams, and a consistent 40-hour weekly schedule.

Participate in the Gradient Development Program, complete technical training, pursue Google Cloud certifications, and collaborate with technical and business leads to translate business requirements into effective AI and machine learning solutions.

Develop generative AI solutions using Python, AWS, LLMs, chatbots, prompt and context engineering, RAG, vector databases, and agentic AI frameworks. Build coding prompts and contexts with GitHub Copilot, Claude Code, and Amazon Q Developer to convert business stories into production code and pull requests. Design autonomous and multi-agent workflows, memory and token-management systems, caching strategies, MCPs, and real-time AI decisioning features. Auto-lending experience and A/B testing expertise are advantageous.

Lead design and implementation of AI and data governance frameworks, privacy guardrails, metadata standards, and compliance with GDPR/HIPAA/CCPA/NIST AI RMF. Monitor and audit models for bias, explainability, and transparency; partner with engineers, stewards, and leaders to embed ethical decision-making, set KPIs, run audits, and produce dashboards and reports to drive governance adoption.

Annotate construction drawings using bounding boxes, polygons, and segmentation masks for computer vision models. Maintain consistent, accurate labels, clean datasets, identify ambiguous cases, and help improve annotation guidelines. Meet weekly quality and throughput targets while communicating data issues to ML engineers. The role requires focused repetitive work, precise instruction-following, independent remote work, and clear written English. It is a remote, 3–6 month contract with potential full-time conversion.

Identify and evaluate enterprise AI and transformation opportunities, quantify business value, and translate business needs into responsible AI use cases. Develop proofs of concept, knowledge bases, and conversational AI agents; facilitate discovery workshops; collaborate with leadership, Technology, Risk, Compliance, and Operations; present recommendations; support adoption through training and communication; and contribute to AI governance, playbooks, and capability building.

Designs and demonstrates computer vision solutions for customer opportunities, including model selection, data strategy, evaluation, edge deployment, and business-case development. The role leads discovery, high-level architecture, proofs of concept, demos, partner engagement, and technical enablement. It requires hands-on expertise across classical computer vision, deep learning, video intelligence, vision language models, model lifecycle management, and edge optimization, plus strong customer-facing and commercial communication skills.

Drives AI adoption across a regional Wealth Management field organization. Builds relationships with advisors and leadership, translates AI capabilities into practical workflows, delivers training and coaching, supports product and technology rollouts, identifies business development opportunities, gathers feedback, and develops strategies to sustain adoption across client engagement, prospecting, campaigns, and growth activities.

Evaluate LLM and automation outputs for accuracy, consistency, and reliability; create annotations and training datasets; research ambiguous cases; support product recognition and classification models; and use AI tools to improve operational efficiency. The role also involves maintaining model and catalog quality through mapping and classification work.

Build functional AI prototypes and MVPs using low-code platforms such as Copilot Studio and Power Platform. Translate business needs into technical specifications, user stories, and workflows; partner with engineering to scale validated concepts into production. Roll out AI productivity tools, support go-to-market and customer success workflows, measure adoption and impact, and apply AI security, privacy, governance, and compliance principles while coordinating with product, architecture, legal, and security stakeholders.

Build and scale agentic AI systems that automate customer experience workflows across Slack and Salesforce. Analyze transcripts, detect SLA risks and escalations, provide self-service operational insights, and develop AI memory architecture. Partner with data, QA, workforce management, and customer advocacy teams to ensure system accuracy, reliability, trustworthy outputs, and measurable operational impact. Productize internal tools and design human escalation patterns for scalable deployment.

Supports research, testing, and implementation of AI solutions for business processes, data analysis, and decision-making. Develops AI prototypes and proof-of-concepts, creates reports and dashboards, documents workflows and best practices, and collaborates with stakeholders to gather requirements and feedback. The role provides practical experience with AI tools, analytics, process automation, and cross-functional project delivery.

Design and deploy production ML/AI architectures on the Databricks platform, lead Generative AI (LLM, RAG, agent) implementations, optimize and scale MLOps pipelines, provide technical pre-sales support, and translate customer feedback into product improvements while mentoring peers and meeting delivery milestones.

Annotates data accurately according to project guidelines, performs quality checks, provides feedback on annotation tools and processes, collaborates with project teams, maintains efficiency, and reports issues for resolution.

Annotate and label data accurately according to project guidelines, conduct quality checks, provide feedback on annotation tools and processes, collaborate with project teams, maintain efficiency, and identify issues for resolution. The role requires strong attention to detail, critical thinking, adaptability, communication skills, and comfort with repetitive computer-based work.

Develop, optimize, test, and maintain prompts for conversational AI, automation, and business applications. Evaluate AI outputs, conduct comparative experiments, improve response quality, and create reusable prompt libraries. Translate business needs into prompting strategies while collaborating with AI engineers, trainers, product teams, and stakeholders. Document standards, support governance, research emerging techniques, and contribute to Generative AI implementation and continuous improvement.