Top AI Jobs in Denver & Boulder, CO

Design, build, and operate large-scale LLM infrastructure and data platforms for training, fine-tuning, and inference. Provision GPU clusters, optimize GPU utilization, implement model lifecycle management, deploy inference frameworks, create evaluation and observability systems, and collaborate with data scientists to productionize AI capabilities. Mentor engineers and enforce MLOps/DataOps best practices.

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.

Serve as the bridge between AI models and field operations to deliver measurable business value. Prepare data for AI readiness and governance, implement and iterate a knowledge-base chatbot, pilot wearable AI and AR onboarding for technicians, build an "AI manager" with agentic workflows, and standardize prompts, automation, and workflows to optimize technician productivity and service quality.

Lead enterprise AI transformation engagements combining C-suite advisory, solution architecture, data catalog strategy, knowledge-graph design, AI governance, and adoption. Design end-to-end architectures (Now Assist, RAG, AI Agents), define governance and metadata strategies, build reusable practice assets, and drive value realization and change management across complex enterprise customers.

Design, build, and deploy AI agents and multi-step automation workflows for Client Experience. Integrate systems via APIs, partner with stakeholders, ensure governance and security, and track metrics for efficiency and customer outcome improvements.

Lead product for AI Corps: define roadmap, metrics, and platform powering fellow recruitment, matching, deployment, and enterprise L&D. Partner with Earned Revenue, Curriculum, Engineering, and Anthropic to run discovery, experiments, and ship high-quality experiences that scale to enterprise customers.

As a Senior AI Engineer, you'll design and implement AI-driven solutions, focusing on full-stack development and production-grade applications while mentoring junior team members.

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.

Develop AI solutions for marketing teams, integrating AI agents and workflows while coaching teams for self-sufficiency in AI use.

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.

Provide legal guidance on Rubrik's AI use and products, ensuring compliance with laws and contracts, drafting and negotiating AI terms, coordinating generative AI review processes, educating employees, and improving AI review controls and workflows across cross-functional teams.

Lead the development and implementation of machine learning solutions, managing a team to drive strategic decisions and innovative business outcomes.

As a Principal AI Engineer, you'll design production-grade systems integrating generative AI in mental healthcare, overseeing architecture and applied research.

Lead Gusto's AI/ML strategy, managing teams in Machine Learning Engineering, AI, and Risk Data Science. Ensure systems are reliable and impactful while fostering a culture of technical excellence.

Senior SRE on the IT Operations team owning reliability, monitoring, and incident response for AI infrastructure. Build automation, CI/CD and Kubernetes tooling, improve observability and documentation, and develop internal full-stack tools using Go or Python. Partner with Infrastructure, Security, and Compliance to scale secure, resilient AI deployment pipelines.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

Lead teams building computer vision and machine learning for AI dash cameras, managing model lifecycle, coordinating with cross-functional teams, and driving AI product development.

Lead strategy and execution for generative and multimodal vision systems, from research to production. Design model architectures, adaptation, evaluation, and deployment pipelines; improve realism, controllability, robustness, and temporal consistency; benchmark and guide model selection; mentor scientists and coordinate cross-functional engineering to ship production-ready AI capabilities.

Build and productionize agentic workflows and RAG pipelines, integrate LLMs into the GTM stack, and develop high-performance backend services and ML pipelines. Connect quoting engine, Salesforce, and data lakes, solve synchronization and architectural challenges, and ensure observability, scalability, and reliability for sales automation tools.

Lead client engagements to assess business challenges, define enterprise data and AI strategies, develop roadmaps, guide solution design and delivery, provide technical leadership and mentoring, and support practice growth via presales and thought leadership.

Lead and scale AI platform and agent engineering teams to build AI infrastructure, agent runtimes, MLOps/LLMOps, prompt and skills lifecycle management, and forward-deployed engineering for healthcare customers. Drive platform reliability, security, observability, enterprise integrations, and operational excellence to deploy AI-powered solutions at scale.

Build, deploy, and maintain AI-powered agents, APIs, and applications for People operations on Snowflake. Turn messy business requirements into production systems, integrate with Workday/Notion/case tools, ensure data governance and security, design multi-model LLM reliability controls, and operate full lifecycle including CI/CD, monitoring, and incident fixes while partnering with non-technical stakeholders.

Lead and grow a distributed engineering team to build and scale AI platforms and agents for healthcare. Own technical strategy, data/retrieval/semantic architectures, ML model serving and MLOps, and deliver HIPAA-compliant, cloud-native systems in partnership with Product, Data Science, and Clinical teams.