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

Design, build, and operate distributed GPU training infrastructure for large neural networks. Optimize multi-node/multi-GPU execution, diagnose compute/memory/network bottlenecks, improve stability and fault tolerance, and partner with research teams to productionize large-model training pipelines for reliable, high-throughput training at scale.

Lead Conversational AI Developer responsible for developing chatbots and virtual assistants, integrating AI technologies, and improving user interactions through data analysis and software development.

Lead design, development, and productionization of ML and LLM systems for patient- and provider-facing healthcare products. Own end-to-end workflows: problem scoping, data, modeling, evaluation, deployment, monitoring, and clinical guardrails. Build evaluation frameworks, infrastructure, and tooling; partner with clinicians and product; mentor engineers and set technical direction to deliver safe, impactful AI features for clinical care.

As a Senior AI Platform Engineer, you will design and build production software for AI Employees, manage technical delivery for client implementations, and ensure system reliability and performance.

Design, build, and operate a secure, scalable Generative AI platform on AWS (including Bedrock) to support production LLM applications in regulated financial environments. Own platform lifecycle: architecture, deployment, operations, monitoring, incident management, reliability, and performance. Implement LLM inference, RAG, Agents, guardrails, observability, security controls, access management, compliance, and cost optimization. Provide production support, reliability engineering, and AI governance.

Design, implement, validate, and deploy advanced machine learning and statistical models across the data science pipeline. Collaborate with product and engineering, mentor peers, translate research into production, and deliver high-impact business insights using large-scale and distributed data platforms.

Build responsive React + TypeScript frontends and scalable Python/Rust backend services and APIs. Integrate with cloud (AWS/GCP/Azure), design databases, CI/CD, tests, and monitor reliability, performance, security, and cost. Optionally develop LLM/RAG/embeddings/vector database AI features.

Build end-to-end AI-native features: responsive React/TypeScript frontends, Python/Rust backends, cloud integrations (AWS/GCP), databases, CI/CD, observability, and optionally LLM/RAG/embeddings-based AI features while improving reliability, performance, security, and cost.

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.

Build and productionize end-to-end AI-powered product features: prototype and iterate on prompts, retrieval flows, evals, backend systems, APIs, data models, and UX to deliver reliable, customer-facing LLM workflows.

Hands-on Senior AI Engineer to design and ship production Generative AI systems: build RAG pipelines, embeddings, vector search, multi-agent orchestration, backend APIs (Python/Flask/FastAPI), deploy cloud-native workloads, implement LLMOps practices, and mentor through code reviews while owning end-to-end delivery.

Lead architecture, design, and delivery of enterprise-scale Agentic AI solutions. Oversee multiple engineering pods, mentor teams, manage client-facing architecture discussions, ensure production-grade MLOps, cloud infrastructure, microservices, observability, and adoption of emerging AI technologies.

Design, build, and operate agentic LLM workflows for delivery: requirements review, automated checks, early risk detection, RAG-backed knowledge bases, human-in-the-loop feedback, monitoring, and reusable components from prototype to production.

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.

Build production-grade infrastructure for GTM AI agents: harden prototypes, write evals and prompts, implement observability, own deployment pipelines on GCP, manage safe database access, monitor and improve agent fleet, and partner with GTM builders to scale reliability.

Build and maintain AWS-based AI infrastructure and services for ML workflows and real-time inference. Implement CI/CD pipelines, integrate LLM/GenAI components, ensure observability, write high-quality code, collaborate cross-functionally, and prototype ML algorithms to turn forensic data into investigation leads.

Design, build, and operate a secure, scalable Generative AI platform on AWS (Bedrock) for production LLM applications in regulated environments. Own architecture, deployment, monitoring, incident management, reliability, governance, cost optimization, and AI evaluation/Responsible AI practices.

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.

Design, build, deploy, and optimize AI/ML models and pipelines (including LLMs and RAG). Integrate models into production, work with cross-functional teams, ensure monitoring and compliance, and contribute to scalable AI systems and automation.

Design, build, fine-tune, and deploy production-grade AI and ML systems (LLMs, RAG). Develop scalable APIs, integrate AI into business systems, monitor model performance, and collaborate with cross-functional teams.