Top AI Engineer Jobs in Denver & Boulder, CO

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 and deploy AI-native products that transform large-scale geospatial data into actionable intelligence. Design multi-agent workflows, integrate frontier LLMs, engineer tool-using agents, and develop data pipelines for anomaly detection and predictive analytics. Ensure secure, scalable cloud integrations and operate production AI systems using DevOps, containerization, orchestration, and CI/CD. Establish feedback and reinforcement mechanisms to improve model reliability while contributing to AI-first engineering standards and documentation.

Design and deploy production AI assistants, copilots, agents, RAG systems, and workflow automation using enterprise data. Build AI backend services, APIs, microservices, and AWS serverless workflows; integrate enterprise platforms; implement observability, responsible AI, human-in-the-loop controls, and traceability. Partner with Data, IT, and business teams to improve operational processes and decision-making while managing production incidents, model rollbacks, performance, accuracy, and cost.

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.

Lead the AI Quality engineering team while remaining hands-on building evaluation infrastructure, observability tooling, diagnostic systems, datasets, annotation workflows, and CI/CD pipelines for production AI agents. Run experiments across prompts, models, routing, caching, and agent harnesses to improve quality, latency, and cost. Partner with AI Core engineering and manage technical direction and team priorities.

Design, build, deploy, and scale production AI agents and platforms for legal workflows. Develop and evaluate agent solutions, optimize performance and cost, redesign business processes, and promote responsible AI adoption. Collaborate with Legal, Enterprise AI, Data, Security, Privacy, and Engineering teams to deliver secure, reliable, scalable solutions. Contribute frameworks, training, governance guidance, and innovative applications using modern LLM and AI techniques.

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.

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.

Lead design and build of an AI-native marketing platform: integrations, multi-agent orchestration, UIs, tooling, analytics, CI/CD, auth/multi-tenancy, and production AI agents to automate marketing workflows and drive adoption across teams.

Build production AI and machine learning software for large-scale computing and AI infrastructure. Responsibilities include developing anomaly detection, forecasting, root cause analysis, LLM workflows, agentic systems, and operational analytics using Go, Python, and React. The role spans data engineering, model development, evaluation, deployment, monitoring, and continuous improvement while collaborating with engineering, architecture, product, and customer teams.

Develop AI data infrastructure, metadata and lineage systems, foundation models, agentic AI platforms, and knowledge-infused learning capabilities. Build and evaluate NLP, LLM, time-series, diffusion, and computer vision systems while improving reproducibility, governance, reasoning, scalability, and trustworthy AI outcomes. Partner with software teams and external collaborators to translate innovative research into production implementations.

Design, build, and ship production LLM-powered features for Gusto Retirement. Implement and integrate LLMs (RAG, agents, prompting, tool loops), make pragmatic architecture decisions, collaborate with product, design, and engineering, write reliable well-tested code, and drive end-to-end execution and iteration.

Build and operate production-grade AI developer tooling, agentic workflows, and shared infrastructure that improve software development across engineering teams. Responsibilities include AI-assisted code review, testing, issue triage, model integrations, evaluations, monitoring, cost controls, adoption enablement, and cross-functional collaboration with security, platform, quality, product, and design teams.

Build and implement AI-powered developer productivity tools, including code review automations, AI personas, reusable prompts, and workflow playbooks. Partner with engineering teams to identify coding workflow improvements, prototype tools in Python, integrate GitHub and CI/CD systems, measure adoption and quality impact, and support enablement through demonstrations, office hours, and training.

Develop and maintain AI-driven autonomous planning, scheduling, and optimization systems for Earth observation satellite operations. Build reinforcement learning algorithms, simulation and evaluation environments, and multi-objective optimization pipelines for satellite constellation tasking. Collaborate with aerospace engineers, mission operators, software engineers, and product teams to translate mission requirements into deployable systems. The role requires strong Java and Spring Boot experience, software engineering fundamentals, and machine learning or optimization expertise.

Build and scale AI infrastructure through Python automation, AWS serverless architectures, EKS containerized workloads, Terraform infrastructure as code, and monitoring. Develop orchestration pipelines for model training and inference, manage large datasets and production availability, reduce recovery times, collaborate with QA and Product, and mentor engineers while maintaining code quality and operational performance.

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.

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.

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.

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.

Design, build, and productionize LLM-powered systems (adaptive content, tutors, evaluators) and AI pipelines (RAG, embeddings, fine-tuning). Prototype and optimize models across modalities, define AI safety/evaluation/observability practices, and collaborate with product and engineering to measure learner outcomes and iterate.

Develop AI-assisted engineering practices and shared tooling for physicists and engineers. Establish standards for LLM use in embedded C++, Rust, RTOS, and kernel development, while measuring productivity, quality, safety, and reliability. Influence AI tooling architecture and workflows across a multidisciplinary engineering organization, supporting experiment analysis, optics, PCB and FPGA design, quantum control software, and safe AI operation in quantum processors.