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

Build production AI agents and agent-powered features from research and prototyping through deployment. Develop evaluation infrastructure with golden datasets, regression suites, and offline/online scoring. Own retrieval and context pipelines, production instrumentation, failure taxonomies, and cost and latency budgets. Partner with data science and product management on experiments and agent behavior, while establishing LLM engineering patterns, guardrails, and reusable tooling.

Owns the strategy, architecture, and implementation of production AI and machine learning systems for personalization, search ranking, relevance, and patient-provider matching. Builds recommendation engines, retrieval models, LLM applications, and scalable ML infrastructure while establishing AI safety, evaluation, and MLOps standards. The role requires hands-on model development, technical leadership, mentoring, and responsible deployment of AI in a regulated mental healthcare environment.

Design, build, test, deploy, monitor, and maintain production LLM-based agentic solutions for Commercial Customer Success. Integrate agents with CRMs and Samsara systems, define evaluation frameworks and success metrics, implement automated tests and monitoring, iterate on models and prompts, and demonstrate impact on customer outcomes and retention.

Own product-layer performance for agentic AI systems in healthcare. Design prompting, retrieval, context, memory, tool use, evaluation, escalation, and human-review workflows. Build production-grounded evaluations, diagnose failures, improve accuracy, reliability, latency, cost, and steerability, and approve model changes. Partner with Product and Engineering while maintaining model cards, intended-use guidance, limitations, and failure-mode documentation for safe, transparent deployment.

Build and deploy AI-powered solutions across Sales, Marketing, and Customer Support. Diagnose workflows, identify constraints, evaluate whether AI is appropriate, and own initiatives from discovery through deployment. Develop LLM applications using prompt engineering, RAG, agentic architectures, orchestration, guardrails, and enterprise integrations. Measure business value through flow metrics, adoption, ROI, and feedback loops while partnering with technical and non-technical stakeholders.

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.

Build and ship production-grade agentic AI workflows across Acquia’s digital experience platform. Architect stateful multi-agent systems using LangGraph, Temporal, and Pydantic; own LangFuse observability, evaluation, and prompt management; establish standards for RAG and tool calling; partner on secure, reliable enterprise architectures; evaluate AI tooling; mentor engineers; and support customer architecture and roadmap discussions.

Take Deepgram's speech models and optimize, compile, and run them on resource-constrained embedded and edge hardware. Work across model compression, runtime performance (C/C++/Rust), RTOS/bare-metal and embedded Linux environments, vendor NPUs/DSPs and inference runtimes, benchmarking, deployment pipelines, OTA updates, and partner with silicon vendors to enable real-time, low-power on-device voice experiences.

Design, prototype, and deploy LLM-powered agents, RAG workflows, and automations that accelerate internal teams and customer-facing product features. Integrate AI systems with SaaS tools, build reusable components and evaluation frameworks, embed with business teams to deliver measurable outcomes, and establish standards for responsible AI, monitoring, and cost control across the company.

Design and implement multi-step agentic AI workflows (orchestration, retrieval, tool use, state management) using frameworks like LangGraph/LangChain. Build retrieval pipelines over proprietary CRE data, evaluate and monitor model quality with human-in-the-loop processes, integrate Anthropic models on AWS Bedrock, implement guardrails and observability, and rapidly prototype features with Product and Design to deliver trustworthy, source-linked AI capabilities that perform real business actions.

Designs and implements scalable AI, machine learning, automation, and generative AI solutions for clients. Responsibilities include assessing AI readiness, developing strategies, leading discovery and solution delivery, integrating AI with enterprise systems, advising on governance and responsible AI, managing client engagements, contributing to reusable AI offerings, and supporting business development. The role requires strong technical, consulting, communication, and stakeholder management skills.

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.

Build and scale AI platform infrastructure for internal agents across AWS/GCP. Integrate identity (Okta, OAuth2/OIDC), implement agentic security, containerized deployments (Docker/Kubernetes), CI/CD and Terraform-based pipelines, run vendor evaluations, and design observability/telemetry for LLM-driven workflows while collaborating with security, identity, and engineering teams.

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.

As a Staff AI Engineer, you'll lead AI product development, overseeing technical direction, system deployment, and model evaluation while collaborating across teams.

The role involves designing AI systems for mental healthcare, focusing on building scalable, production-grade systems that enhance patient engagement and clinical operations. Responsibilities include setting technical standards and ensuring safety and reliability while working with advanced AI technologies.

Designs, develops, deploys, evaluates, secures, and maintains enterprise AI, machine learning, generative AI, and LLM solutions for government applications. Responsibilities include data preparation, model training and monitoring, evaluation, red-team testing, safety controls, documentation, governance, privacy protection, cybersecurity compliance, architecture reviews, and stakeholder collaboration. The role translates mission and technical requirements into production-ready, responsible AI capabilities.

Design, develop, and maintain enterprise AI applications, including conversational assistants, copilots, autonomous agents, RAG solutions, and workflow automation. Build scalable APIs and microservices integrating foundation models and enterprise systems. Apply prompt and context engineering, memory management, observability, governance, and performance optimization. Collaborate with cross-functional teams to identify automation opportunities and participate in Agile development, code reviews, CI/CD, and DevSecOps.

Integrate and fine-tune existing AI models within production software, evaluate model performance, build evaluation frameworks, configure observability, develop Python AI features, and troubleshoot real-world performance. Collaborate with data science, engineering, the CTO, and cross-functional teams to improve products and identify AI innovation opportunities. This role focuses on applying existing models rather than designing new machine learning models.

Design, build, deploy, and scale production-grade AI applications using LLMs and RAG architectures. Develop backend services, application integrations, and scalable cloud infrastructure across AWS, Azure, or GCP. Use Docker and Kubernetes to containerize and orchestrate systems, while optimizing performance, reliability, and monitoring. Collaborate with cross-functional teams on architecture, code reviews, documentation, and engineering practices.

Set technical direction for AI platform architecture across multiple engineering teams while building production Python systems. Develop agent orchestration, tooling, governance, evaluation, cloud-native services, data persistence, security, testing, CI/CD, and observability patterns. Serve as a technical advisor and embedded expert on complex initiatives, influence roadmaps and staffing, and mentor engineers across the organization.

Own shared AI platform components and build production AI features, including LLM orchestration, prompt workflows, evaluation, observability, and deployment. Develop scalable SaaS backend systems, APIs, data pipelines, search, background processing, and PostgreSQL solutions. Lead CI/CD and AI tooling adoption, establish technical direction, mentor engineers, and translate ambiguous requirements into reliable systems.

Architect, build, and deploy production-grade AI/ML platforms and LLM/RAG solutions. Lead system design, CI/CD and Terraform-based IaC, ensure reliability and monitoring, mentor engineers, run incident response, and prototype emerging AI technologies.

Customer-facing engineer who designs, builds, and productionizes GenAI applications (RAG, multi-agent, Text2SQL, fine-tuning). Own production rollouts, advise customers, collaborate with product and engineering, present at conferences, and optimize/evaluate models and deployments across cloud platforms.

Build and operate production AI capabilities across enterprise platforms, including agent runtimes, RAG, model routing, tool calling, guardrails, evaluation, monitoring, and cost optimization. Ship reliable, safe, measurable AI features; develop evaluation frameworks and quality controls; monitor production behavior; and collaborate with client-facing product, engineering, platform, backend, and security teams to deliver practical AI solutions.