Top AI Engineer Jobs in Denver & Boulder, CO

Design, build, and own scalable data pipelines and evaluation systems that power production AI features and internal analytics. Ensure data quality across ingestion, modeling, and reporting, collaborate with ML and analytics teams, deploy and monitor ML systems, and establish standards for data work and evaluation.

Own an AI portfolio across a business domain, identify and prioritize opportunities, and personally build production agentic solutions end to end. Establish reusable patterns, evaluations, human-in-the-loop controls, and measurable business outcomes. Partner with functional leaders, earn stakeholder trust, guide platform choices, and improve team capability through mentoring, design and code reviews, and interviewing. Build reliable, secure, scalable applications around AI systems across frontend and backend layers.

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 coach teams to design, develop, and deliver production-grade AI and data solutions for clients. Manage client accounts, ensure quality and timelines, analyze complex datasets, implement best practices, and drive continuous improvement while fostering client relationships and technical excellence.

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.

Build and operate Coinbase's LLM and agent platform: unified model gateway, AI Hub, secure microVM agent runtime, knowledge base integrations, AI FinOps, observability and evaluation tooling, and production-grade agent deployments with teams across the company.

Design, build, and operate agentic AI systems end-to-end for Professional Services. Translate stakeholder needs into secure, scalable multi-agent architectures, define performance benchmarks, manage deployments and incident response, advise leadership, mentor teammates, and maintain runbooks and production tooling including on-call responsibilities.

Build and maintain the Python-based backend engine for GitLab Duo Chat, including agentic LangGraph flows, the Flow Registry, and Duo Workflow Service. Integrate chat capabilities with GitLab’s Rails monolith and GraphQL API, develop secure and tested high-scale features, optimize performance, review code, address technical debt, and collaborate cross-functionally. Participate in weekday, weekend, and occasional nighttime Tier 2 or Tier 3 on-call rotations.

Own product-layer performance for agentic AI systems in healthcare. Design prompting, retrieval, context, memory, tool use, evaluation, escalation, and human-review workflows. Build production-grounded evaluations, diagnose failures, improve accuracy, reliability, latency, cost, and steerability, and approve model changes. Partner with Product and Engineering while maintaining model cards, intended-use guidance, limitations, and failure-mode documentation for safe, transparent deployment.

Build and own internal AI agents, automations, workflows, and full-stack applications for the GTM organization. Lead solutions from prototyping through production scaling, beta testing, maintenance, documentation, and user training. Integrate secure data sources, evaluate internal tools against vendors, manage LLM costs and performance, apply UX best practices, and determine when agentic workflows are appropriate. Stay current with LLMs and agent orchestration technologies.

Design and implement AI systems and scalable ML models (Python, TensorFlow); build/maintain data pipelines and integrations; apply NLP (NLTK) and deep learning for analytics; deploy and optimize models (including open-weight LLMs); collaborate with stakeholders and continuously learn to improve AI solutions and deployment reliability.

Design, build, deploy, and maintain resilient, scalable backend services and platform components for Toast's Voice AI platform. Lead cross-cutting architecture and projects, integrate AI platforms (LLMs), implement observability and evaluation for AI systems, set engineering standards, and mentor engineers.

Owns the Retail AI GTM Pod and leads the deployment, integration, scaling, and maintenance of AI applications that improve sales and onboarding productivity. Responsibilities include evaluating build-versus-buy solutions, managing technical sprints, developing RAG and agentic workflows, integrating Salesforce, Snowflake, and Taskray, enforcing application security and data integrity, monitoring adoption, and enabling nontechnical teams to use AI effectively.

Build AI applications, agents, integrations, and automated workflows that transform technical information into customer documentation. Partner with the Documentation team to use LLMs, agentic AI, and automation to improve content creation, maintenance, publishing, and delivery. Reduce manual effort, increase access to technical knowledge, and identify additional customer-facing workflows where AI can improve efficiency and experience.

Build and deploy AI-powered solutions across Sales, Marketing, and Customer Support. Diagnose workflows, identify constraints, evaluate whether AI is appropriate, and own initiatives from discovery through deployment. Develop LLM applications using prompt engineering, RAG, agentic architectures, orchestration, guardrails, and enterprise integrations. Measure business value through flow metrics, adoption, ROI, and feedback loops while partnering with technical and non-technical stakeholders.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

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 the technical direction, architecture, and delivery of Benchling’s enterprise agentic AI portfolio. Build production systems integrating foundation models, orchestration, tools, memory, evaluation, observability, security, and workflow automation. Establish CI/CD, testing, deployment, and governance practices; graduate prototypes into reliable systems; and provide production support. Enable internal teams through SDKs, templates, coaching, and standards while partnering with product, data, infrastructure, and security teams. Lead technical hiring and mentor engineers as a hands-on founding engineer.

Build and ship production-grade agentic AI workflows across Acquia’s digital experience platform. Architect stateful multi-agent systems using LangGraph, Temporal, and Pydantic; own LangFuse observability, evaluation, and prompt management; establish standards for RAG and tool calling; partner on secure, reliable enterprise architectures; evaluate AI tooling; mentor engineers; and support customer architecture and roadmap discussions.

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.

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.

Take Deepgram's speech models and optimize, compile, and run them on resource-constrained embedded and edge hardware. Work across model compression, runtime performance (C/C++/Rust), RTOS/bare-metal and embedded Linux environments, vendor NPUs/DSPs and inference runtimes, benchmarking, deployment pipelines, OTA updates, and partner with silicon vendors to enable real-time, low-power on-device voice experiences.

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.

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.