Top AI Jobs in Denver & Boulder, CO

Design, build, deploy, and iterate production-ready AI agents and LLM-based solutions that integrate with enterprise workflows. Rapidly prototype, evaluate, and optimize agents for performance, cost, and adoption. Partner across functions to redesign workflows, contribute to an AI Center of Excellence, and ensure responsible, secure, and scalable agent development.

Lead architecture and delivery of AI-native features for a multi-tenant SaaS platform. Drive cross-functional technical initiatives, design scalable microservice systems, integrate LLMs and agent frameworks, set engineering standards, and mentor engineers to elevate technical execution.

Design, build, deploy, and maintain scalable, resilient backend services for a Voice AI platform. Lead cross-cutting projects, make architectural decisions, integrate AI platforms, implement observability and evaluation frameworks, and mentor engineers.

Own end-to-end voice platform migration and scaling: LiveKit migration, real-time turn-taking, VAD, SIP/WebRTC, multi-agent handoff, training/fine-tuning voice models, inference cost optimization, and retrieval-based grounding using Supabase/pgvector.

The Junior-Midlevel Developer will build and enhance Dumroo.ai's AI-powered education platform, collaborating on software components, data pipelines, and machine learning features.

Lead and grow the AI/ML Engineering team, manage core AI/ML platform development, ensure production-grade service operational excellence, and develop product strategies.

The Senior Director Analyst in Analytics & AI leads research on market trends, technologies and best practices to advise clients and drive strategy. Responsibilities include creating research content, providing actionable insights, mentoring team members, and presenting at conferences.

The Partner Solutions Architect will manage technical relationships with partners, design solutions, assist in deployments, and gather feedback to influence product roadmap.

Lead AI delivery-driven projects from initiation through closure, providing strategic direction, executive communication, risk and escalation management, roadmap alignment with technical/product/AI stakeholders, and mentoring PMs to ensure timely, high-quality SaaS and GenAI customer outcomes.

Build and scale AI platform infrastructure for internal agents across AWS/GCP. Integrate identity (Okta, OAuth2/OIDC), implement agentic security, containerized deployments (Docker/Kubernetes), CI/CD and Terraform-based pipelines, run vendor evaluations, and design observability/telemetry for LLM-driven workflows while collaborating with security, identity, and engineering teams.

The Principal AI/ML Engineer will develop AI/ML solutions for hardware and firmware validation, enhance test efficiency, and collaborate across teams to implement data-driven analytics and automation.

Build end-to-end full-stack AI features on a Ruby on Rails backend and React frontend, design and run experiments (A/B tests and AI evaluations), implement AI infrastructure for reliable LLM inference, and iterate quickly to scale production-ready LLM-powered products.

Architect and scale an internal AI platform and APIs to integrate LLMs into developer workflows. Build autonomous agents, RAG systems, and production-grade tooling to boost developer productivity, ensure reliability, security, and cost-efficiency, and collaborate across product, infrastructure, and security teams.

Design and implement AI agents and Model Context Protocols to integrate enterprise systems (NetSuite, Zuora, Jira) with LLMs. Build secure, SOX-compliant automation and orchestration workflows, measure KPIs, and partner with Finance, IT, and Security to operationalize AI across enterprise applications.

Design, build, and ship production LLM-powered features for Gusto Retirement. Implement and integrate LLMs (RAG, agents, prompting, tool loops), make pragmatic architecture decisions, collaborate with product, design, and engineering, write reliable well-tested code, and drive end-to-end execution and iteration.

Build the core AI platform and high-performance Rust backend powering an AI-native trading copilot. Implement streaming responses, low-latency tool execution, caching, orchestration of models and tools, secure APIs, auditing/tracing, and safe, auditable trading action infrastructure. Partner with product and frontend to enable AI-native UX and translate trading domain requirements into resilient platform primitives.

Lead ownership of reliability, evaluation, and safety for an AI copilot in a trading platform. Build eval systems, benchmarks, automated quality gates, monitoring, and model improvement loops; partner with engineering and product to define safe tool contracts and ensure predictable, auditable assistant behavior in trading workflows.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

Lead end-to-end product development of enterprise-grade AI-powered healthcare solutions (generative AI, LLMs, RAG, automation). Drive roadmap, customer validation, cross-functional execution, measurable adoption, and business outcomes while mentoring product practices.

Lead the architecture and technical vision for AI-powered GTM systems, defining patterns for low-code and custom agentic pipelines. Build reference architectures, design multi-system integrations and RAG/LLM orchestration, evaluate AI tools, drive governance and observability, and partner with GTM leadership to deliver production-grade, scalable solutions.

Lead strategy and technical direction for generative and multimodal vision systems. Build end-to-end model workflows, improve realism, controllability, and robustness, drive evaluation and production-readiness, partner with engineering to deploy optimized inference, and mentor scientists while owning a major technical pillar from research to production.

Drive deployment of AI solutions for field services: prepare data governance, integrate knowledge-base chatbots, enable wearable AI and AR onboarding, build agentic "AI manager" workflows, and optimize prompts and automation to improve technician productivity and service quality.

Operate as the bridge between predictive AI outputs and frontline training by governing human validation, mitigating bias, designing automated workflows that trigger adaptive microlearning, auditing AI flags for accuracy and relevance, translating insights for stakeholders, and continuously measuring and improving AI-driven training effectiveness.

Serve as the bridge between AI models and field operations to implement AI-driven tools (chatbots, wearables, AR, agentic workflows). Prepare AI-ready data, enforce governance, deploy and iterate knowledge-base chatbots, design AR onboarding, create an "AI manager" for technicians, and build prompt libraries and automation to optimize workflows and improve field technician efficiency and service quality.

Partner with business leaders to define AI strategy, set KPIs, and deliver executive reporting; prototype AI solutions (e.g., Google AI Studio); remove operational obstacles across ServiceNow, Jira, and automated workflows; enforce data governance and privacy to enable enterprise AI adoption and measurable ROI.