Top AI Engineer Jobs in Denver & Boulder, CO

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

Build and deploy generative AI solutions, including APIs, MCP servers, AI agents, LLM integrations, and full-stack applications. Develop scalable AWS cloud services, observability systems, evaluation pipelines, and production agent workflows. Collaborate with Product, UX, Security, Legal, and engineering teams to design interfaces, adapt AI tools to company codebases, and support customer experience and developer productivity use cases.

Build and integrate secure AI assistants and chatbots into geospatial intelligence, analytics, visualization, and mission-support applications. Develop LLM workflows using RAG, prompt orchestration, tool calling, and conversation context. Connect AI services to enterprise data sources and APIs while enforcing identity, authorization, provenance, auditing, and safety controls. Deploy and operate services in Kubernetes and OpenShift, including classified or disconnected environments. Evaluate AI quality, security, latency, and usability.

Leads complex data science and AI initiatives, developing and deploying machine-learning solutions centered on LLMs, agentic workflows, and RAG systems. Translates stakeholder needs into technical roadmaps, mentors analysts and data scientists, manages project budgets and schedules, ensures quality, and designs reliable AI applications using embeddings, vector databases, retrieval systems, and AI workflow platforms.

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 own evaluation infrastructure, observability tooling, datasets, annotation workflows, and diagnostic systems for production AI agents. Lead experiments across prompts, models, routing, caching, cost, and latency. Diagnose failures across planning, tool use, and agent execution, while partnering with AI Core engineering. Manage and grow the AI Quality team as a hands-on player-coach, contributing directly to production code and technical direction.

Build and operate Affirm’s AI platform, including LLM and MCP gateways, agent runtimes, evaluation services, and governance controls. Design highly available backend systems, manage identity, quotas, routing, caching, reliability, observability, and security for AI workloads. Own projects from design through launch and on-call, establish engineering standards, collaborate across teams, mentor engineers, and help scale AI systems into production.

Build production AI/ML software for supercomputers and AI infrastructure, including anomaly detection, forecasting, root cause analysis, automation, and operational intelligence. Develop services with Go, Python, and React; integrate machine learning models, LLM workflows, agentic systems, and analytics. Own the lifecycle from data engineering and model development through deployment, monitoring, and improvement while collaborating with engineering, architecture, product, and customers.

Designs and deploys AI-powered features and scalable AI/ML services across SaaS products. Responsibilities include translating business problems into AI solutions, integrating large language models, preparing data, evaluating model performance, writing Python, building secure deployment infrastructure, and collaborating with product, data, and cloud operations teams. The role requires production AI/ML experience, scalable data infrastructure expertise, AWS familiarity, and modern DevOps practices.

Build and deploy AI-powered tools for Rula’s business and clinical teams by observing workflows, rapidly delivering production software, measuring usage and impact, and enabling teams to operate tools independently. The role requires privacy-by-design practices, rigorous AI evaluation, customer-centric discovery, and integration with complex legacy systems. Staff engineers may optimize LLM costs and quality while creating reusable building blocks for future teams.

Leads complex AI and cloud-native incubation projects, designing and implementing scalable services across public cloud, datacenter, and edge environments. Develops Generative AI, microservices, and distributed-system solutions; evaluates emerging technologies through prototypes and research spikes; guides architecture, security, reliability, testing, CI/CD, and observability. Leads small multidisciplinary engineering teams, collaborates with stakeholders, mentors engineers, and advances innovative capabilities across multiple parallel projects.

Design, build, deploy, and operate production AI agents and multi-agent systems for insurance workflows. Responsibilities include agent orchestration, APIs, RAG pipelines, evaluation frameworks, regression testing, observability, production troubleshooting, and reliability improvements. The role requires hands-on Python software engineering, LLM application development, cloud infrastructure, and collaboration in end-to-end product pods.

Lead the technical direction, architecture, and development of Compass, an LLM-powered agent platform. Design multi-agent systems, MCP and A2A integrations, orchestration, evaluation, and observability infrastructure. Drive cross-team technical projects, production support, engineering quality, mentoring, and adoption of AI-assisted development practices. Partner with Product, QA, Design, Operations, and Engineering stakeholders to deliver reliable agentic systems.

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.

Leads global teams developing production AI, GenAI, RAG, agentic workflow, and MLOps solutions for health systems and health plans. Translates clinical, financial, and operational challenges into HIPAA-compliant use cases involving clinical decision support, population health, revenue cycle, research, and optimization. Oversees healthcare data science engagements, model governance, executive stakeholder communications, client delivery, and team development.

Design, build, and deploy AI-enabled production software for clients. Participate in client meetings, live prototyping, demos, requirements gathering, and own end-to-end product outcomes. Use AI development tools to accelerate engineering while validating and correcting generated code. Ship customer-facing projects, maintain integrations, hosting, and domain configurations, and collaborate in a hybrid team environment.

Own Cresta’s web platform, CMS architecture, component system, marketing instrumentation, localization, SEO/AEO tooling, and GTM integrations. Build systems that enable marketers to ship independently, while developing, deploying, evaluating, and optimizing AI agents for marketing use cases. Partner with product, engineering, design, and marketing teams to take AI agents from prototype to production, establish guardrails and human review processes, and influence the product roadmap.

Own Cresta’s marketing web platform, including architecture, CMS, component systems, analytics instrumentation, localization, SEO, and AEO infrastructure. Build tooling that enables marketers to ship independently and develop, deploy, evaluate, and optimize AI agents for marketing use cases. Partner with design, marketing operations, product, and engineering to deliver production systems, establish guardrails, and influence the AI platform roadmap.

Build and deploy production machine learning systems for personalized recommendations, fraud detection, risk scoring, ranking, classification, and anomaly detection. Own the full ML lifecycle, including mathematical formulation, feature engineering, training, evaluation, A/B testing, deployment, monitoring, and reliability. Develop low-latency real-time inference services using large-scale donor and transaction data, collaborate with Platform and DevOps teams, and mentor engineers on ML best practices and scientific rigor.

Design, build, and operate GiveCampus’s AI platform, including shared context and memory, identity and access controls, agent registries, governance, evaluations, monitoring, and security guardrails. Build reusable infrastructure and templates for internal AI builders, support agent orchestration and deployment, and measure business impact. Partner with product, engineering, and departmental AI teams while growing into the company’s technical AI lead.

Build developer tools for Modular’s MAX AI inference stack, including profiling, tracing, debugging, correctness validation, metrics, dashboards, and agent-native workflows. Integrate tooling across MAX, Mojo, driver APIs, and orchestration layers while supporting heterogeneous hardware. Collaborate with kernel, model, and serving engineers to improve developer productivity, performance analysis, and deployment health.

Supports enterprise AI infrastructure by building secure financial data pipelines, deploying production code and machine learning models, monitoring live systems, troubleshooting bottlenecks, and exposing AI capabilities through internal APIs. Collaborates with senior engineers and cross-functional technical teams while tracking code quality, deployment speed, and system stability.

Designs, implements, and supports AI-enabled enterprise application integrations, intelligent automation, APIs, workflows, and data transformations. Evaluates technical approaches, develops proofs of concept, and partners with business, security, technology, and vendors. Responsibilities include AI governance, testing, deployment, monitoring, troubleshooting, access administration, documentation, and production support. The engineer resolves complex incidents, improves reliability and security, and researches emerging AI and integration technologies.

Leads AI/ML strategy and initiatives, overseeing projects from problem definition and data collection through model development, deployment, monitoring, and improvement. Builds and mentors AI/ML teams, evaluates emerging technologies, collaborates with software engineering and DevOps teams, manages budgets and timelines, communicates findings to stakeholders, and drives innovation through research, patents, and publications. The role requires expertise in advanced AI/ML techniques, generative AI, cloud platforms, big data, and MLOps.

Build and own ClickUp’s AI evaluation systems, agent harness, self-improvement loops, model benchmarking, routing, and fallback strategies. Architect scalable backend and AI platform services for LLM deployment, orchestration, monitoring, logging, and automated evaluation. Improve reliability, privacy, security, and compliance while integrating AI features into the core product and collaborating cross-functionally.