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

Early-career AI Engineer building customer-facing generative AI and agentic solutions. Rapidly prototype and convert prototypes to production MVPs, implement LLM-based reasoning, RAG pipelines, and multi-agent orchestration. Run experiments and benchmarks, contribute to MLOps standards, model lifecycle, monitoring, security, and governance while collaborating with cross-functional teams and cloud-native AI stacks.

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.

This role involves defining AI architecture, improving system quality, leading teams, and building production-ready AI systems at Drata.

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.

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.

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.

Own end-to-end voice platform migration and scaling: LiveKit migration, real-time turn-taking, VAD, SIP/WebRTC, multi-agent handoff, training/fine-tuning voice models, inference cost optimization, and retrieval-based grounding using Supabase/pgvector.

Architect and scale an internal AI platform and APIs to integrate LLMs into developer workflows. Build autonomous agents, RAG systems, and production-grade tooling to boost developer productivity, ensure reliability, security, and cost-efficiency, and collaborate across product, infrastructure, and security teams.

Design, prototype, and productionize generative AI features and agentic workflows. Build Python services, TypeScript/React UIs, data pipelines, evaluation and instrumentation, and safeguards. Partner with product and stakeholders to measure impact, ensure reliability, and mentor engineers.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful 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.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

As a Sr. Applied AI Engineer at Zapier, you will build and enhance AI platform capabilities, focusing on LLM Ops and ML Ops to support scalable AI development across teams.

Lead AI security strategy and guardrails, design and review AI gateways and tooling, scale InfoSec review processes, reduce AI-driven security noise, drive remediation and investigations, and mentor security and platform engineers.

Design, build, and operate production AI and data systems for marketing: LLM/agent orchestration, retrieval pipelines, data pipelines, CDP and warehouse architecture, automation of manual workflows, data quality and reliability, stakeholder collaboration, and mentoring. Ship production-grade Python and SQL solutions and evaluate/monitor AI systems.

Own the technical vision and architecture for an AI platform of agents, skills, and tools. Scale retrieval/knowledge systems, build developer experience, orchestration, observability, and evaluation/feedback loops. Align cross-division engineering strategy and drive platform adoption.

Build end-to-end full-stack AI features on a Ruby on Rails backend and React frontend, including LLM inference infrastructure, RAG, AI agents, and evaluation/observability. Design and run experiments (A/B tests), optimize systems for production performance and reliability, and iterate rapidly with product and design partners.

Design and build scalable, automated evaluation pipelines and infrastructure to validate speech, audio, and multimodal models. Translate research benchmarks into enforceable pass/fail gates, run batch and streaming tests, operate canaries and continuous monitoring, integrate quality gates into CI/CD, and partner with research, training, inference, and infra teams to prevent regressions and ensure production model quality.

As a Senior AI/Data Engineer, you will design and maintain scalable data and AI infrastructures, partnering with cross-functional teams to deliver reliable data systems and support AI-driven business capabilities.

Build end-to-end internal AI agents and workflow automations: discover high-value opportunities, redesign processes, implement LLM-based agents with integrations, deploy and operate solutions, and ensure security, compliance, monitoring, and adoption across GTM teams.

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

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, develop, and maintain Java-based cloud-native applications on GCP to support image capture, processing, storage, and distribution. Build REST APIs, image processing pipelines, and AI-enabled features (classification, OCR, redaction, similarity search). Ensure security, compliance, CI/CD, infrastructure-as-code, observability, and cross-functional collaboration.