Top AI Engineer Jobs in Denver & Boulder, CO

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.

Develop AI-enabled software solutions for internal digital transformation initiatives. Design, code, test, debug, and support scalable applications using modern engineering practices. Apply machine learning tools, automate workflows, analyze data, contribute to APIs, microservices, and cloud-native architecture, and translate business requirements into technical designs. Review junior team members’ work, provide technical guidance, and collaborate with internal stakeholders to deliver reliable enterprise applications.

Designs and deploys scalable AI and machine learning systems using Python and TensorFlow. Responsibilities include data wrangling, complex analysis, NLP, deep learning, data pipeline management, data integration, and model optimization. The role also involves stakeholder collaboration, aligning AI initiatives with business goals, mentoring junior team members, and contributing to data infrastructure and engineering practices.

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.

Design, build, and operate production AI automations and agentic workflows for Toast’s customer care systems. Integrate LLMs with Salesforce Service Cloud, Amazon Connect, and workflow platforms; develop autonomous actions with human oversight; test, monitor, and improve system reliability, accuracy, latency, and cost. Collaborate with software engineers, Salesforce developers, and care operations stakeholders to translate workflows into scalable intelligent solutions.

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 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.

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.

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.

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.

Lead design and delivery of advanced data and ML solutions, performing exploratory analysis, statistical modeling, and visualization to inform strategy. Manage and coach teams, collaborate with clients to validate outcomes, and improve analytics processes and data quality to drive business decisions.

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.