Top Remote Artificial Intelligence Engineer Jobs in Denver & Boulder, CO

Build and maintain AI-powered go-to-market systems that automate sales, expansion, marketing, forecasting, handoffs, and post-sales workflows. Optimize processes, maintain CRM data quality, test and monitor automations, connect GTM tools, and translate operational needs into scalable technical solutions. The role uses AI and iPaaS tools to surface next-best actions, improve deal velocity, and support customer lifecycle operations.

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.

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.

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.

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.

Leads the design, integration, and delivery of production-grade AI, generative AI, agentic AI, and MLOps solutions for clients. Serves as technical lead, translates requirements into scalable architectures, mentors engineers, manages technical risks and timelines, supports sales proposals, and presents recommendations to stakeholders. Responsibilities include application integration, security, observability, evaluation, model serving, documentation, testing, and operational readiness.

Designs, implements, validates, benchmarks, troubleshoots, and supports accelerated compute and high-performance storage infrastructure for enterprise AI workloads. Conducts discovery, migrations, installations, configuration, testing, documentation, and client knowledge transfer. Collaborates with project managers, architects, sales teams, partners, and stakeholders to manage scope, timelines, risks, and deliverables. The role requires client-facing consulting, technical documentation, infrastructure troubleshooting, occasional overtime, and travel to client sites or company locations.

Implements, validates, troubleshoots, and optimizes high-performance AI network fabrics supporting GPU clusters and distributed workloads. Configures InfiniBand, Ethernet, RoCE, routing, congestion management, telemetry, cabling, and fabric-management systems. Performs installation, burn-in, performance testing, documentation, knowledge transfer, and client handoffs. Contributes to statements of work, implementation plans, and solution validation while managing project scope, schedules, budgets, and customer communication.

Architect and build production AI security systems spanning endpoint sensors and cloud-scale infrastructure. Lead autonomous cyber defense capabilities, including detection, threat hunting, response, adversary simulation, secure agent execution, and model evaluation. Establish technical direction, benchmarks, and safety practices while mentoring senior engineers and researchers. Carry frontier AI research into the Falcon platform and represent the company through publications, open source, standards, and industry collaboration.

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.

Architect, build, deploy, and scale production AI agents and platforms for legal workflows. Develop secure, reliable, and cost-effective AI solutions; prototype and evaluate agents; optimize performance; redesign business processes; and collaborate with Legal, Data, Security, Privacy, and Engineering teams. Contribute to AI training, governance, responsible AI adoption, and enterprise standards while translating technical capabilities into measurable business outcomes.

Lead technical sales for Deepgram’s Voice Agent platform, owning discovery, architecture, proofs of concept, technical evaluations, and customer production launches. Partner with account executives on enterprise deals, carry a growth quota, build demos and integrations, develop sales enablement, provide product feedback, and collaborate with telephony, contact center, and LLM partners. The role requires deep voice-agent expertise across telephony, orchestration, streaming pipelines, and speech and language models.

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.

Owns the design, development, evaluation, launch, and operation of customer-facing AI agents for support, lending, sales, and ordering. Builds production LLM agents with tool use, retrieval, workflows, safety guardrails, monitoring, and human handoffs. Integrates agents with business systems, develops evaluation suites from real-world data, measures business outcomes, and mentors engineers while partnering with nontechnical business owners.

Fine-tune and align LLMs for specialized financial tasks; build agentic solutions with LangChain/Autogen; engineer ML pipelines for large financial datasets; deploy, monitor and scale production models; track AI research and integrate emerging tooling.

Design and deploy production-ready LLM pipelines and agentic workflows, automate large-scale multimodal dataset pipelines, monitor and improve agent performance, and integrate AI agents with 3D scene composition. The role also involves process documentation, incident response, and continuous improvement, with collaboration across the engine team and company founders.

Build reinforcement learning environments to evaluate AI systems on software engineering tasks involving MCP tools. Responsibilities include creating reproducible environments, deterministic verification, golden reference solutions, bug fixes, feature development, codebase refactoring, and performance optimization. The role is remote, flexible, contract-based, and output-focused, with no prior AI experience required.

Create reinforcement learning environments to evaluate AI systems solving complex software engineering tasks with MCP tools. Responsibilities include designing reproducible environments, deterministic verification, golden reference solutions, bug fixes, feature implementation, codebase refactoring, and performance optimization across software codebases.

Build and operate AI-native applications and agentic systems end to end, from stakeholder discovery and prototyping through deployment, adoption, and ongoing iteration. Design retrieval, context, tool integrations, orchestration, evaluation, observability, and cost controls. Develop scalable event-driven systems, collaborate with platform and product teams, document solutions, train users, and mentor less experienced engineers.

Develop and enhance enterprise AI agents and orchestration capabilities using Python, LangGraph, and LangChain. Build guided workflows, conversational AI experiences, planning and tool-calling functionality, and integrations with backend systems. Support MCP and A2A communication, AI observability with Arize, troubleshooting, reliability improvements, and delivery of centralized AI platform capabilities across multiple business use cases.

Combines wastewater process engineering with AI, machine learning, process modeling, digital twins, and optimization. Responsibilities include analyzing treatment data, developing biological and chemical process models, evaluating and troubleshooting facilities, conducting experiments, testing technologies, and implementing predictive or optimization solutions. The engineer collaborates with multidisciplinary project teams and utility clients to improve treatment performance, reliability, energy use, and operational decision-making. Python programming and wastewater modeling platforms are preferred, along with wastewater treatment, nutrient removal, data science, and technical communication expertise.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Build and own conversational voice AI products end-to-end: architect, implement, and deploy the platform. Design infrastructure, develop front-end and back-end systems, deliver reliable customer outcomes, and collaborate directly with the CTO to scale the product for enterprise customers.

The Staff AI Engineer will analyze AI quality, implement improvements, manage projects, collaborate with teams, and shape AI capabilities in a remote-first environment.