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

Design and scale a reusable Customer Success AI platform: build foundational AI infrastructure (orchestration, memory, context, workflow engines), optimize model routing, observability, governance, and security, lead architecture and standards, mentor engineers, and partner cross-functionally to deliver enterprise-grade, multi-product AI capabilities.

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.

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 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.

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.

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.

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.

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

Own reliability, monitoring, and incident response for AI infrastructure; build automation and CI/CD tooling; manage Kubernetes/Docker production workloads; partner with infrastructure, security, and compliance; improve observability and documentation; develop internal full‑stack tooling in Go or Python.

Senior SRE on the IT Operations team owning reliability, monitoring, and incident response for AI infrastructure. Build automation, CI/CD and Kubernetes tooling, improve observability and documentation, and develop internal full-stack tools using Go or Python. Partner with Infrastructure, Security, and Compliance to scale secure, resilient AI deployment pipelines.

Own and administer Salesforce and NetSuite, design and deploy Claude-based agent workflows and automations, ensure data quality and HIPAA-compliant governance, manage integrations and quote-to-cash processes, run sandboxed release/change management, document systems, and coach non-technical teams to adopt and extend AI-enabled automations.

Design, implement, and maintain core infrastructure and tooling to enable agentic AI development. Drive AI enablement, developer relations, training, and shared platform tooling to support engineering teams and production LLM/agent integrations.

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 own scalable data pipelines and evaluation systems that power production AI features and internal analytics. Ensure data quality across ingestion, modeling, and reporting, collaborate with ML and analytics teams, deploy and monitor ML systems, and establish standards for data work and evaluation.

The Senior Software Engineer for Support AI will develop voice and chat systems for customer support, driving projects from concept to production, while collaborating across teams and improving AI capabilities through integration and data insights.

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.

Build, deploy, and maintain AI-powered agents, APIs, and applications for People operations on Snowflake. Turn messy business requirements into production systems, integrate with Workday/Notion/case tools, ensure data governance and security, design multi-model LLM reliability controls, and operate full lifecycle including CI/CD, monitoring, and incident fixes while partnering with non-technical stakeholders.

Design, build, and deploy AI-powered legal applications using LLMs, RAG, and agentic AI. Partner with legal stakeholders to translate workflows, build document ingestion and vector search pipelines, ship production-grade features, implement governance/security controls, and mentor/legal users while aligning solutions with enterprise architecture and monitoring quality and evaluation metrics.

Develop, test, and maintain applications using Java, Python, or Node.js. Write clean, efficient code, debug and optimize, collaborate with cross-functional teams, use data structures/algorithms, work with SQL/NoSQL databases, participate in code reviews, and stay current on AI/ML concepts, agents, and automation tools.

Lead architecture and hands-on implementation of large-scale Generative AI and agentic systems. Build production-ready RAG, embedding fine-tuning, vector search, and MLOps pipelines on Google Cloud. Advise clients in pre-sales, mentor engineers, and ensure reliability, latency, cost, and responsible-AI controls for high-stakes deployments.

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 intelligent agent systems on an AWS-native GenAI platform. Implement AI-enabled features and RAG/agent workflows, design and deploy small services, maintain CI/CD, participate in on-call support, contribute to design reviews, mentor junior AI engineers, and collaborate with product, data, and platform teams.