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.

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.

Build evaluation infrastructure, observability tooling, diagnostic systems, datasets, annotation workflows, and experiments for production AI agents. Lead a hands-on AI Quality engineering team, improve agent quality, latency, and cost, and partner with AI Core engineering. The role requires developing evaluation pipelines, scorers, dashboards, CI/CD systems, and model-testing frameworks while managing team priorities and technical direction.

Owns enterprise AI security assessment, architecture, and control programs. The role evaluates AI systems, vendors, platforms, and shadow AI; designs secure architectures, guardrails, identity models, sandboxes, gateways, and runtime controls; and develops detection and response capabilities for prompt injection, jailbreaks, data leakage, and tool misuse. The engineer partners with governance, identity, platform, engineering, and security operations teams, advises senior leaders, drives remediation, and mentors security professionals.

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.

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.

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 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.

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.

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.

Build and ship production AI products, including LLM coaching experiences, agentic workflows, retrieval and orchestration systems. Develop predictive, classification, ranking, and recommendation models; establish evaluation and quality processes; create trusted data pipelines and knowledge sources; and integrate AI with enterprise systems. The role requires strong ownership, safety-conscious engineering, evidence-based recommendations, and collaboration across Engineering, Product, and Data leadership.

Unpaid AI engineering internship to design, build, and deploy ML solutions (NLP, computer vision, recommenders). Work on data preprocessing, model training/fine-tuning, containerized FastAPI microservices, and scalable deployments on Google Cloud Run. Contribute to code reviews, GitHub repos, and integration of third-party AI APIs.

Build and scale production agentic AI systems for a regulated fintech platform. Responsibilities include architecting AI infrastructure, improving context, memory and state management, developing evaluation systems, integrating AI features with TypeScript and Node.js, enforcing security and privacy controls, and establishing AI-native engineering practices. The role requires reliable, production-scale AI experience and comfort working in a fast-moving startup environment.

Design, build, and optimize agentic AI solutions, including LLM prompts, agent workflows, MCP tools, tool-calling frameworks, RAG systems, and context orchestration. Develop evaluation methods, integrate enterprise systems and APIs, support deployment and monitoring, and ensure solutions meet security, governance, compliance, and responsible AI standards. The role is client-facing and involves collaboration across product, architecture, and engineering teams.

Design, build, and launch AI-focused software capabilities using Python and distributed-systems principles. Own initiatives from solutioning and architecture through production implementation and launch while remaining hands-on with code. Collaborate with Nextiva engineering and product teams, Google Gemini, and other external technology partners to extend AI capabilities across partner environments and the Nextiva ecosystem. Solve ambiguous early-stage technical problems and contribute to system design and architecture decisions.

Designs, builds, and evaluates agentic AI systems for healthcare workflows such as documentation, coding, denial management, and revenue cycle automation. Develops long-horizon agent behavior, evaluation frameworks, benchmarks, feedback loops, monitoring, and production safeguards. Partners with research, ML engineering, clinical, product, and engineering teams to prototype and deploy reliable, explainable, auditable AI systems using LLMs, machine learning, and agentic technologies.

Operate and improve production conversational AI systems for chat, voice, and customer-support workflows. Build agent workflows, API integrations, guardrails, retrieval systems, automated evaluations, analytics, data pipelines, Python services, and internal tools. Support testing, monitoring, security, governance, and production releases while developing broader full-stack AI engineering expertise.

Leads hands-on design and delivery of production agentic AI systems for the NBA platform. Builds multi-step AI workflows, integrates LLMs, implements RAG and vector search, develops evaluation and reliability frameworks, and embeds AI-powered analysis into user-facing interfaces. Leads a small engineering team and contractors, partners with product and frontend teams, and ensures safe, auditable AI operation in a regulated healthcare environment.

Architects and develops AI-powered systems, tools, agents, backend services, APIs, and experimentation frameworks using foundational AI models. Integrates AI capabilities with microservices, AWS, Snowflake, and complex data systems while ensuring scalability, reliability, and maintainability. Translates business problems into technical solutions, prototypes AI applications, collaborates with product and engineering teams, evaluates emerging AI technologies, and documents system designs and integration patterns.

Lead architecture and implementation of autonomous inbound/outbound AI agents, build scalable LLM ops and signal-intelligence data pipelines, integrate identity resolution and third-party CRM systems, and maintain production-ready, well-documented systems for Series B diligence.

Lead the architecture, development, evaluation, and operation of enterprise autonomous AI agents integrated with Salesforce. Build stateful multi-agent workflows using LangGraph, Claude, and MCP; implement evaluation, observability, security, and PII protection; optimize model routing and performance; and establish engineering standards, API contracts, and architecture roadmaps. The role requires extensive software engineering and production LLM experience, strong debugging and stakeholder communication skills, and advanced English proficiency.

Build AI agent evaluation and trace intelligence features, run benchmarks and experiments on Mastra primitives, and use findings to improve agent accuracy, configuration, and behavior. Develop tools that help developers understand agent performance and enable automatic self-improvement through evaluations, suggestions, and rollouts. This hands-on role combines experimentation, benchmarking, and production framework engineering in a fully remote environment.