Top AI Automation Jobs in Denver & Boulder, CO

Build scalable QC systems and validation pipelines for RL training data: define quality standards, design metrics and experiments, audit supplier datasets, partner with vendors to diagnose failures, and integrate learnings into infrastructure and vendor tooling.

The Director of AI Automation leads AI-driven automation systems, translating strategic goals into practical solutions, managing teams, and enhancing operational efficiency.

Join a talent pool to teach, author, or advise on AI-powered automation. Deliver live workshops, create course materials, design exercises, shape curriculum, review technical content, and support program updates. Part-time (10–15 hrs/week), remote with collaboration in European time zones. Roles span junior to senior levels and emphasize hands-on experience with automation platforms and real-world operational workflows.

Lead design, prototyping, deployment, and continuous improvement of AI-powered applications, agents, copilots, and automations. Integrate AI with portals, workflows, data platforms, and AWS; define reusable patterns (RAG, prompts, agent workflows); ensure secure, observable, and cost-effective production systems; measure adoption and ROI; and mentor engineers, contractors, and vendors.

The AI Automation Engineer develops AI-driven systems to automate sales and marketing workflows, partnering with various teams to enhance revenue generation and process efficiency.

Design, build, and maintain AI-enabled workflow orchestration and automation systems for legal service delivery. Implement integrations between AI platforms, CRMs, document systems, client portals, and payment systems. Develop event-driven pipelines, retrieval and prompt orchestration, agent workflows, and observability. Monitor reliability, troubleshoot integrations, support compliance and governance, and document architectures and operational standards.

Develop and maintain an AI-driven pipeline to auto-generate, process, and publish real-life and digital quests. Integrate AI frameworks, design scalable automation workflows, analyze user data to improve generation quality, build API integrations, participate in architecture and documentation, test with pytest/unittest, and collaborate with cross-functional teams to validate LLM outputs against real-world data.