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

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.

Build and operate Coinbase's LLM and agent platform: unified model gateway, AI Hub, secure microVM agent runtime, knowledge base integrations, AI FinOps, observability and evaluation tooling, and production-grade agent deployments with teams across the company.

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 Senior Agentic AI Engineer, you'll develop core systems for AI agents, focusing on reliability, observability, and production readiness, working hands-on with distributed systems and orchestration.

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.

Lead architecture and technical direction for a cloud-native, multi-tenant Student Journey Platform. Design and enforce standards for Kubernetes-based AI services (AKS), event streaming, ML model serving, autoscaling, observability, and compliance. Ensure scalable, reliable, cost-efficient infrastructure, resolve architectural risks and production issues, and align platform decisions with business growth and SLAs.

Fine-tune and align LLMs for specialized financial tasks; build agentic solutions with LangChain/Autogen; engineer ML pipelines for large financial datasets; deploy, monitor and scale production models; track AI research and integrate emerging tooling.

Design, build, and deploy AI-powered workflows, internal tools, assistants, and platform capabilities to automate operational processes. Integrate LLM APIs, RAG/vector retrieval, and backend systems, collaborate with product and operations, and deliver production-ready solutions that reduce manual work and scale GreenLight's operations.

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.

The AI Automation Lead will architect and deliver multi-agent AI workflows, develop full-stack applications, and lead technical decisions across engineering teams.

Act as the technical presales lead for AI agent and cloud identity security across North America: scope engagements, design and run POCs and architectures across AWS/Azure/GCP, secure non-human and machine identities, create repeatable technical assets, and partner with account teams to close deals.

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

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 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.

The Senior AI Engineer will design and build autonomous AI agents for mental health applications, focusing on LLM integration and reinforcement learning techniques for user interactions.

As a Principal AI Engineer, you will lead the development of the AI infrastructure at Exactera, focusing on building complex knowledge graphs, hybrid retrieval systems, and compliance-grade AI solutions for tax advisory applications.

Build and deploy high-impact AI-native products, architect agent-driven workflows, and collaborate with teams to integrate cutting-edge AI solutions into production systems.

Design, build, and operate multi-agent AI backends that generate fast, accurate genomics-based health guidance. Implement planners, routers, workers, evaluators, RAG pipelines, parallel execution, caching, evaluation benchmarks, and production TypeScript services integrating databases, queues, and data systems while ensuring scientific accuracy, safety, and low latency.

Develop AI solutions for marketing teams, integrating AI agents and workflows while coaching teams for self-sufficiency in AI use.

The AI Engineer will build AI-driven workflows, integrating customer requirements into scalable solutions while managing both front-end and back-end elements in a cloud environment.

The Senior AI Engineer will develop AI-driven solutions using LLMs, focusing on prompt engineering, integration, research, and data utilization for payment systems.

The AI Engineer develops advanced AI systems, manages datasets, creates and integrates machine learning models, and collaborates with teams to enhance applications.

As an AI Engineer, you will develop and optimize AI systems, focusing on LLM integration, vector databases, and RAG pipelines, collaborating with various teams to ensure cutting-edge performance.

This role involves developing and optimizing LLM applications, architecting workflows, and integrating AI functionalities. Strong experience in AI/ML and collaboration is required.

The role involves building and scaling LLM-powered applications, managing the full product lifecycle, ensuring system reliability, and collaborating across teams to deliver impactful AI-driven features.