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

As an AI Engineer, you will build and scale AI business applications, focusing on generative AI. Responsibilities include rapid prototyping, applying software engineering best practices, and providing production support.

Lead teams of data scientists and ML engineers to design and deploy AI solutions using Oracle tools and modern AI/ML practices. Manage clients, translate business problems into AI architectures, drive innovation, ensure quality, and mentor staff.

Design, implement, and deploy advanced AI/ML solutions using Oracle and modern ML tooling. Analyze complex problems, mentor junior staff, build client relationships, manage data pipelines and model lifecycle (CI/CD and monitoring), and contribute to cross-functional delivery while upholding professional and ethical standards.

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.

Lead design and implementation of agentic AI workflows and integrations for Acquia DAM. Build, ship, and observe production AI features (auto-tagging, search, governance), set AI engineering standards, evaluate models and tooling, mentor engineers, and represent AI architecture to product and customers.

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.

Senior individual contributor who defines enterprise AI architecture, builds reusable platform components and governance controls, drives observability and cost attribution, mentors engineers, and partners across Security, Legal, Data, and product teams to enable secure, measurable AI adoption.

Build and operate production AI capabilities—agents, prompts, connectors, automations, and telemetry—to improve productivity. Deliver reusable components, integrate AI with enterprise systems, enforce responsible AI controls, instrument costs and ROI, and collaborate across security, legal, data, and business teams to scale safe, measurable AI adoption.

Hands-on individual contributor building and operating enterprise AI capabilities: agents, prompts, connectors, automations, and telemetry. Partners with stakeholders to convert workflows into production-quality, secure, measurable AI solutions, create reusable components, enforce governance controls, optimize cost and ROI, and enable cross-functional adoption and observability.

Build and deploy production micro-apps and LLM-powered agents on a governed GCP platform. Integrate AI with systems (Salesforce, Slack, NetSuite, SQL), harden apps with auth, secrets, CI/CD, logging and monitoring, automate maintenance, and implement evaluation and observability for model quality and drift.

Contribute to building and hardening micro-apps and AI integrations on a shared GCP platform. Complete bug fixes and small features, participate in code and design reviews, apply security and software development best practices, monitor applications and follow runbooks, and use AI tools to assist development, testing, and documentation while learning from senior engineers.

Lead development and delivery of production-grade AI/GenAI solutions for health systems, architect RAG and agentic workflows, manage global healthcare data science teams, ensure HIPAA-compliant MLOps and model governance, engage executive health-system stakeholders, and translate clinical workflows into measurable AI use cases across clinical, financial, and operational domains.

Lead development of AI and GenAI solutions for health systems, advise stakeholders, guide teams through ambiguity, ensure operational excellence and compliance, and implement MLOps, model governance, and production AI use cases against clinical data sources.

Design, build, and operate agentic AI systems end-to-end for Professional Services. Translate stakeholder needs into secure, scalable multi-agent architectures, define performance benchmarks, manage deployments and incident response, advise leadership, mentor teammates, and maintain runbooks and production tooling including on-call responsibilities.

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

Design and deliver LLM-powered, agentic AI systems to automate customer support and compliance. Build scalable, secure backend infrastructure in Python and Go, integrate third-party models/APIs, implement grounding and guardrails, and measure performance. Lead end-to-end projects, mentor engineers, conduct code reviews, and participate in on-call and incident response to improve resolution times and reduce operational costs.

Design, build, and operate large-scale LLM infrastructure and data platforms for training, fine-tuning, and inference. Provision GPU clusters, optimize GPU utilization, implement model lifecycle management, deploy inference frameworks, create evaluation and observability systems, and collaborate with data scientists to productionize AI capabilities. Mentor engineers and enforce MLOps/DataOps best practices.

Design, build, deploy, and iterate production-ready AI agents and LLM-based solutions that integrate with enterprise workflows. Rapidly prototype, evaluate, and optimize agents for performance, cost, and adoption. Partner across functions to redesign workflows, contribute to an AI Center of Excellence, and ensure responsible, secure, and scalable agent development.

Design, build, deploy, and maintain scalable, resilient backend services for a Voice AI platform. Lead cross-cutting projects, make architectural decisions, integrate AI platforms, implement observability and evaluation frameworks, and mentor engineers.

Build AI-augmented BI dashboards, semantic layers, and AI-ready metadata. Use AI-assisted tools to accelerate SQL and dashboard development, productionize ML outputs into BI, enforce data quality and governance, and present insights to business stakeholders including C-suite.