Top AI Automation Jobs in Denver & Boulder, CO

Build end-to-end internal AI agents and workflow automations: discover high-value opportunities, redesign processes, implement LLM-based agents with integrations, deploy and operate solutions, and ensure security, compliance, monitoring, and adoption across GTM teams.

Hands-on technical leader who designs, builds, and deploys production-grade AI automation and agentic workflows. Responsibilities include rapid prototyping, RAG and document-intelligence systems, API integrations, orchestration, monitoring, reusable AI assets, and mentoring a small engineering team to deliver frequent releases and measurable business impact.

Own and administer Salesforce and NetSuite while designing and deploying production-grade AI-driven automations and agent workflows. Ensure data governance, PHI/HIPAA compliance, release management, and quote-to-cash integrity. Partner cross-functionally to prioritize work, coach teams, document systems, and measure impact of automation on operations and revenue.

Build and ship AI-native systems for a fintech app: AI agents, RAG/retrieval systems, tool-calling and automation for onboarding, KYC, support, risk, CRM and operations. Work with product and engineering to design reliable, low-hallucination, cost- and latency-aware solutions and turn manual processes into scalable AI-enabled systems.

Lead end-to-end design of AI-enabled networking experiences: convert ambiguous AI opportunities into product concepts, prototypes, and shipped features. Define AI-first interactions, data and telemetry requirements, reusable UX patterns for control and explainability, and partner with platform, engineering, and research teams to deliver trustworthy, measurable intelligence features.

The SME will develop a curriculum on AI for finance, creating engaging content and videos to train finance professionals in AI applications. They must leverage their extensive finance experience and AI understanding to enhance learner success.

Lead strategy and execution of AI, automation and GenAI initiatives for Finance. Partner with finance leaders and technology teams to discover opportunities, design and deploy AI/NLP solutions, define governance, measure KPIs/ROI, and drive stakeholder adoption and ethical, compliant implementation.

Build and deploy production micro-apps and LLM-powered agents on a governed GCP platform. Integrate AI with systems (Salesforce, Slack, NetSuite, SQL), harden apps with auth, secrets, CI/CD, logging and monitoring, automate maintenance, and implement evaluation and observability for model quality and drift.

Contribute to building and hardening micro-apps and AI integrations on a shared GCP platform. Complete bug fixes and small features, participate in code and design reviews, apply security and software development best practices, monitor applications and follow runbooks, and use AI tools to assist development, testing, and documentation while learning from senior engineers.

The AI Deployment Engineer will build, deploy, and scale AI solutions while collaborating across departments to ensure successful implementation and support of operational workflows.

Design and deliver LLM-powered, agentic AI systems to automate customer support and compliance. Build scalable, secure backend infrastructure in Python and Go, integrate third-party models/APIs, implement grounding and guardrails, and measure performance. Lead end-to-end projects, mentor engineers, conduct code reviews, and participate in on-call and incident response to improve resolution times and reduce operational costs.

Lead intake, roadmap, and governance for an AI automation/platform team. Harden and ship business-built AI apps to production with secure GCP platforms, define acceptance criteria and responsible-AI gates, manage vendor buy-vs-build decisions, drive adoption, measure outcomes, and manage AI economics and stakeholder communication.

Support AI project teams by executing workstreams, preparing client-facing presentations and documentation, assisting implementation activities, maintaining required certifications, using relevant technology, communicating with stakeholders, and traveling as needed for client engagements.

Lead client engagements in insurance data, analytics, and AI: define roadmaps, run discovery sessions, design solutions, manage delivery and teams, develop proposals, and ensure DataOps, cloud (AWS), governance, and best practices across AI and analytics initiatives.

Lead AI-driven reconciliation and automation to improve encounter data quality. Own model performance, identify root causes, partner with technical and business teams to enhance models, prioritize roadmap, and scale automation while communicating results to stakeholders.

Designs, builds, tests, and deploys AI-enabled automations and copilots for GTM and RevOps. Creates reusable agent templates, prompt libraries, and orchestration patterns; implements end-to-end API integrations; monitors performance; documents and hands off production solutions; and trains/coaches business teams on safe, responsible AI usage and prompting best practices.

The AI Automation Engineer will apply AI in finance operations to automate processes like AP, AR, and reconciliation, ensuring compliance and performance measurement.

The Principal Consultant leads AI and digital transformation engagements, guides clients in identifying AI opportunities, builds strategies and roadmaps, implements solutions, and develops teams to achieve measurable business outcomes.

The role leads AI adoption across business functions, prioritizing use cases, building business cases, and driving organizational change necessary for successful implementation. It requires collaboration with IT leadership and the presentation of ROI to business stakeholders.

Lead the design and implementation of AI agents and automation across pro-code and no-code platforms. Define repeatable automation patterns, integrate AI platforms with business tools via APIs, build RAG pipelines and vector DB integrations, write Python scripts for complex integrations, and guide technical decisions while working closely with the VP of AI to review agent performance and scale solutions.

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.

Build and own an internal AI-powered recruiting pipeline: end-to-end LLM-based sourcing, screening, AI interviews, evaluation logic, and deep integrations with ATS and scheduling to automate hiring 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.

Verify and reconcile hearing data across sources, build and maintain lightweight automation and AI-assisted verification workflows, troubleshoot data pipelines, respond to tickets, and send verified schedules to attorneys while meeting daily quality targets.