Top AI Engineer Jobs in Denver & Boulder, CO

Build agentic AI products that enable customers to create custom detections and workflows from operational camera data. Own projects from customer discovery through design, implementation, production operation, and ongoing optimization. Develop backend services, APIs, and customer-facing experiences; partner with platform, machine learning, product, and design teams. Improve AI accuracy, reliability, performance, and cost while mentoring engineers and shaping technical architecture.

Lead Samsara’s AI initiatives across the software development lifecycle, including developer agents and SRE AI. Set strategy, select experiments, make build-versus-buy decisions, control infrastructure and token costs, scale automated incident response, and build trust in AI-driven actions. Manage and grow a team of five to ten senior engineers while partnering directly with R&D leaders and developers.

Leads client-facing healthcare AI and data science engagements, managing teams that design, develop, validate, and deploy HIPAA-compliant AI and GenAI solutions. Responsibilities include architecture, healthcare process analysis, model and LLM selection, RAG and agentic workflow development, data engineering, MLOps, stakeholder presentations, team coaching, and delivery oversight across clinical, financial, and operational health system use cases.

Build and own production Vision AI systems end to end, including model selection, inference pipelines, evaluation, monitoring, and customer-facing features. Shape architecture, collaborate with product, design, and engineering teams, and use AI coding tools to accelerate development. Establish high standards for quality, testing, reliability, security, and privacy while mentoring engineers and taking ambiguous ideas from prototype to production.

Architect and build Toast’s Vision AI platform, including video ingestion, processing, storage, inference pipelines, evaluation, and monitoring. Develop production ML services and customer-facing computer vision features, make architecture decisions, write significant code, and guide technical direction. Partner across teams, mentor engineers, establish reliability, security, and privacy standards, and promote AI-assisted development. The role requires deep distributed-systems and cloud expertise, production ML experience, video familiarity, and the ability to take ambiguous ideas from prototype to customer-ready products.

Apply established machine learning techniques—including clustering, classification, and anomaly detection—to ICS/OT cybersecurity data. Integrate model outputs into batch and near-real-time pipelines, evaluate third-party models, maintain reliable data contracts and observability, and troubleshoot production issues such as data drift and output quality. Collaborate with AI engineers, data engineers, and product teams while clearly communicating model behavior, uncertainty, and downstream use.

Designs, develops, integrates, tests, and deploys AI/ML applications and MLOps solutions for clients. Builds model-serving workflows, APIs, RAG solutions, enterprise integrations, and containerized NVIDIA AI services. Troubleshoots software, model, data, API, and platform issues; creates technical documentation and client handoff materials; contributes to project estimates and implementation plans. This client-facing role also manages scope, schedule, budget, deliverables, and occasional travel to client sites.

Designs, develops, and refines agentic AI and generative AI solutions across SAP environments. Translates client requirements into scalable application designs, builds prototypes and integrations, evaluates model behavior and prompts, and supports SAP ERP and finance transformation initiatives. The role also involves client collaboration, troubleshooting, technical recommendations, project delivery, and mentoring junior team members.

Build full-stack Vision AI products for restaurant operators, including backend APIs, video pipelines, AI model integrations, monitoring, and customer-facing interfaces. Own platform components, contribute to architecture, collaborate across product and engineering teams, and use AI-assisted development tools. The role requires production software experience, cloud infrastructure expertise, event-driven systems knowledge, and comfort working across backend, frontend, data pipelines, and machine learning integrations.

Designs and develops Azure-based generative AI solutions, including conversational applications, retrieval-augmented generation pipelines, agent workflows, cloud-native microservices, and LLM evaluation systems. The role uses Python, Azure AI Services, Azure OpenAI, LangGraph, vector databases, and DevOps practices to deploy and maintain client solutions. Responsibilities also include managing workstreams, communicating technical tradeoffs, reviewing deliverables, mentoring junior colleagues, and collaborating with clients.

Leads client-facing design and delivery of agentic and generative AI solutions on Azure. Oversees architecture, agent orchestration, RAG pipelines, LLMOps, deployment, testing, security, monitoring, and maintenance. Manages workstreams, budgets, timelines, and deliverables while coordinating with clients and engineering teams. Coaches engineers, reviews technical work, evaluates model behavior, and guides AI-assisted development using Python, C#, and modern AI tools.

Leads client-facing applied AI engineering engagements in healthcare, from solution design through deployment. Translates clinical and health services needs into AI requirements, architectures, and roadmaps; designs RAG, memory, prompt, and agent orchestration components; guides data, model integration, and cloud implementation; evaluates model performance; advises on AI platforms and governance; and develops LLMOps and AgentOps practices. Coaches teams, resolves delivery challenges, and communicates technical decisions to clients and leaders.

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.

Designs and delivers production AI agents, assistants, and automations across CRM and revenue systems. Owns Salesforce architecture, including Apex, Lightning Web Components, Flow, CPQ, security, integrations, and data models. Leads AI evaluation, cost management, monitoring, and ROI measurement. Integrates Salesforce with SAP, DocuSign, HubSpot, Snowflake, and AWS services. Manages DevOps releases, technical standards, Agile projects, user acceptance testing, adoption, and engineering mentorship.

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.

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.

Lead the use of AI and automation across the DevOps lifecycle to accelerate software delivery. Define engineering principles, evaluate AI tools, implement security and compliance guardrails, and measure improvements in delivery performance and developer experience. Provide technical direction across Microsoft Commerce, mentor senior engineers, lead architecture reviews, and influence stakeholders without direct authority. Apply expertise in DevOps, SRE, AI-enabled applications, safe deployment, observability, and regulated engineering environments.

Build analytical and simulation-based performance models for large-scale AI inference deployments on Positron hardware. Model parallelism, networking, batching, latency, throughput, and mixed accelerator configurations; validate projections against hardware measurements; calibrate models as systems evolve; and create customer-facing sizing and performance-estimation tools. Partner with customers, product, and chip-modeling teams to compare deployments, explain performance drivers, and communicate assumptions and confidence levels.

Lead the architecture and development of backend platforms and agentic AI systems. Design scalable distributed services, APIs, runtimes, orchestration, integrations, evaluation systems, and observability capabilities. Drive ambiguous initiatives from concept through production, ensure reliability and safety, collaborate with customers and product teams, and mentor engineers while remaining hands-on with coding and operations.