Top AI Engineer Jobs in Denver & Boulder, CO

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.

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 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.

Build and operate secure, scalable production infrastructure for agentic AI platforms across AWS and Azure. Responsibilities include CI/CD automation, infrastructure as code, cloud security, governance, monitoring, observability, model lifecycle management, drift detection, incident response, networking, resilience, and cost optimization. The role establishes production-readiness standards, supports AI deployments, and partners with engineering, product, security, compliance, and business teams.

Drive adoption of AI-powered utility solutions through pre-sales consulting and post-sales enablement. Gather requirements, architect and demonstrate computer vision and ML workflows, lead RFI/RFP support, guide deployments and integrations, complete security audits, deliver customer training, and create technical presentations. The role requires strong AI/ML knowledge, customer-facing communication, scripting experience, and willingness to travel domestically and internationally.

Develops statistical, machine learning, AI, and predictive solutions to solve complex business problems. Mines and analyzes large datasets, builds algorithms and automated data processes, validates models, and communicates insights to business and product leaders. Designs enterprise AI applications, including generative AI and RAG solutions, while supporting data governance, visualization, security, scalability, monitoring, and responsible AI practices.

Deploys and operationalizes AI solutions across the enterprise, translating business needs into technical requirements and ensuring data, infrastructure, security, governance, scalability, and adoption readiness. The role configures integrations, data pipelines, workflows, and AI services; converts prototypes into production solutions; troubleshoots deployments; enables stakeholder adoption; and creates repeatable implementation practices. It partners with business, product, data science, AI engineering, and platform teams, particularly within regulated environments.

Designs and operates production-quality AI framework, runtime, benchmarking, performance, automation, and observability components. Benchmarks, profiles, debugs, and optimizes large language model training and inference across GPUs and Microsoft hardware. The role drives projects from definition through deployment, investigates cross-stack reliability issues, partners with research, infrastructure, model, and hardware teams, contributes to technical standards, and mentors engineers.

Own the architecture and implementation of Life360’s cross-functional AI agent ecosystem. Define reusable agent standards, identity and authorization models, evaluation and regression infrastructure, execution frameworks, retrieval systems, telemetry, and cost controls. Build production components, guide build-versus-buy decisions, establish shared architecture and standards, validate systems through live use cases, and mentor senior engineers across AI Platforms, AI DevX, and Agentic PDLC.

Lead technical vision and architecture for AI frameworks, performance, benchmarking, and tooling. Drive cross-team alignment, mentor engineers, ship high-performance distributed software, and improve model onboarding, runtime performance, reliability, and Azure hardware efficiency.

Leads architecture, development, and delivery of secure cloud, data, process intelligence, and AI solutions for federal clients. Builds AWS cloud-native services, scalable data platforms, Celonis process-mining solutions, and agentic AI workflows using Amazon Bedrock and Azure OpenAI. Owns technical direction, engineering standards, security, testing, observability, deployment automation, and reusable product packaging. Supports A-123 continuous controls monitoring, agency implementations, demonstrations, and cloud environment deployments while collaborating with AWS and Celonis partners.

Develop and maintain production AI-powered software for mission-critical USPS systems. Design distributed, microservice-based architectures, improve reliability and scalability, identify bottlenecks, and modernize existing systems. Work across the technology stack, communicate architectural decisions, mentor junior developers, and use AI and automation tools to improve engineering outcomes. The role is remote and requires the ability to obtain Public Trust clearance.