Top AI Jobs in Denver & Boulder, CO

Build AI-powered workflows, agents, integrations, and internal tools that automate People Operations. Partner with People, Engineering, and IT to modernize onboarding and other employee processes, while implementing secure access controls, human oversight, escalation paths, and production-ready architectures. Prototype with users, iterate quickly, and measure improvements in efficiency, employee experience, and operational consistency.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Leads Fusion’s Technical Architecture practice while personally building production AI and LLM tooling for Professional Services. Responsibilities include managing and mentoring architects, setting architecture standards, supporting enterprise presales and regulated customers, modernizing delivery infrastructure, establishing AI security guardrails, evaluating emerging AI capabilities, and driving adoption through training and enablement. The role requires deep Salesforce or ServiceNow expertise, hands-on software development, enterprise integration knowledge, and executive-level customer engagement.

Leads Unanet’s internal data strategy and architecture for enterprise AI. Defines the shared data foundation, target architecture, governance standards, roadmap, and reusable approaches for AI assistants, agents, and workflows. Evaluates platforms and integration options, validates technical solutions, and coordinates IT, Security, Legal, Engineering, business owners, and system specialists. Advises senior leaders, establishes data quality and stewardship requirements, and helps determine when data is ready to support scalable AI initiatives.

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.

Own the reliability and quality of an AI copilot for a trading platform. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement workflows. Partner with engineering and product to create safe, auditable tool interactions and improve assistant behavior. Develop domain expertise in trading, risk, margin, execution, and portfolio reasoning while operating across model, backend, and frontend systems.

Leads an AI engineering team of 4–8 engineers while remaining hands-on with architecture, coding, code reviews, and debugging. Owns delivery of production AI systems, including LLM applications, agentic workflows, document extraction, ETL pipelines, observability, monitoring, and model evaluation. Partners with product, platform, data engineering, recruiting, and customers to establish technical direction, hire talent, and deliver reliable, scalable AI features.

Leads Utility’s AI practice by defining its roadmap, overseeing AI research and deployment, identifying client opportunities, mentoring AI teams, and establishing responsible AI practices. The role combines technical leadership, product strategy, client advisory, and operational execution across machine learning, generative AI, and intelligent automation initiatives.

Own production operations for workplace AI platforms, including configuration, integrations, observability, secrets, access controls, model and prompt changes, benchmarking, and incident response. Translate technical findings into governance, risk, compliance, and training materials while collaborating with Security, Legal, HR, and business stakeholders. Investigate failures, implement mitigations, document root causes, and maintain reliable, secure AI services.

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.

Supports AI engineering initiatives at Vantor, contributing to spatial intelligence solutions and helping develop technologies that enable customers to understand current and future conditions. The posting provides limited role-specific responsibilities but emphasizes problem-solving, innovation, collaboration, and mission-focused work. Applicants must qualify as a U.S. Person under applicable export control and ITAR requirements.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Design and implement AI agents, plugins, automations, and enterprise integrations for HR Technology platforms. Lead requirements gathering, technical analysis, solution design, testing, documentation, deployment, security, and data integrity efforts across HR, IT, and business stakeholders. Integrate platforms including ServiceNow, Workday, and other enterprise applications while supporting the HR automation and AI roadmap.

Build and manage AI-powered workflows, agents, automations, and internal applications for Deal Desk and GTM teams. Identify automation opportunities, develop prototypes, scale solutions to production, integrate enterprise data and APIs, implement human validation and audit controls, optimize LLM costs, document tools, measure adoption, and train users. The role requires Deal Desk or RevOps experience plus practical knowledge of AI models, APIs, scripting, SQL/SOQL, Python, CPQ, CLM, Salesforce, and agentic platforms.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Develops research methods for context discovery, retrieval, multimodal representation, reasoning, tool calling, metadata management, and trustworthy AI data pipelines. Builds and evaluates foundation models across language, vision, time series, and scientific applications. The role combines applied research with hands-on engineering, including MLOps, agentic AI, knowledge-infused learning, pipeline optimization, and collaboration with software teams and external partners.

Lead customer-facing architecture for AI data platforms and AI Factory environments. Design data-centric solutions for RAG, inference, and training workflows; size GPU and storage environments; optimize data movement, retrieval, latency, throughput, cost, and reliability. Partner with customers, sales, and Product Management to qualify opportunities, influence product roadmaps, create reference architectures, and develop scalable go-to-market solutions.

Own strategic customer relationships and lead deployment of AI workflows into supplier operations. Map customer SOPs and data sources, identify high-ROI AI opportunities, translate needs into product and implementation requirements, drive adoption, partner with product/engineering, build playbooks, track value, and surface expansion opportunities across global teams.

Owns the backlog, sprint cadence, releases, and production health of enterprise AI products. Translates product strategy into requirements, user stories, work packages, and sprint goals while coordinating engineers, data scientists, business partners, and dependent teams. Leads Scrum ceremonies, PI planning, testing, release communications, and uptime monitoring. Applies AI tooling to improve delivery and uses technical fluency to prototype, review pull requests, and support responsible AI implementation.

Lead data science engagements with customers to acquire, clean, analyze, and model data; develop scalable operational models and evaluation methods; coach engineers on best practices; drive solution strategy, ethics, and industry thought leadership; write extensible code and collaborate across teams to deliver cloud-based AI solutions.

Lead hands-on engineering for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Define APIs and abstractions, make architecture and scalability decisions, improve observability and reliability, and lead a small team of senior engineers through technical reviews, mentorship, and production delivery.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Leads enterprise AI-native engineering initiatives by researching and prototyping AI tools, designing AI-enhanced workflows, creating reference architectures, and developing proofs of concept. Owns AI training and enablement, including documentation, presentations, tutorials, demonstrations, and coaching. Evaluates tooling, supports adoption, communicates technical changes to leadership, hosts engineering office hours, and establishes scalable standards across the organization.

Build and integrate secure AI assistants and chatbots into geospatial intelligence and mission-support applications. Develop LLM workflows using RAG, prompt orchestration, tool calling, and conversational context; connect AI services to enterprise data sources and APIs; and enforce access controls, provenance, auditing, and safety measures. Deploy and operate services in Kubernetes and OpenShift, including classified or disconnected environments, while evaluating AI quality, security, latency, and usability.