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

Build and deploy Guidewire’s production AI claims agent, including prompt and context architecture, RAG, tool integrations, human handoffs, evaluation systems, observability, and voice interactions. Improve accuracy, containment, resolution, latency, and reliability through production trace analysis and disciplined experimentation. Collaborate with insurance carriers to shape workflows, balance autonomy and risk, and harden the system for regulated claims data and real-world policyholder interactions.

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.

Build AI-powered workflows, agents, integrations, and internal tools that automate People Operations. Partner with People, Engineering, and IT to modernize onboarding and other employee processes, while implementing secure access controls, human oversight, escalation paths, and production-ready architectures. Prototype with users, iterate quickly, and measure improvements in efficiency, employee experience, and operational consistency.

Leads customer-facing AI advisory engagements, identifying use cases and designing end-to-end AI deployments across model selection, training, inference, data pipelines, and evaluation. Develops proof-of-value prototypes, enables sales and partner teams, presents at conferences, publishes technical content, and advises enterprise customers on AI strategy. Collaborates across engineering, sales, marketing, alliances, and enablement while independently driving research and production initiatives.

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.

Lead the architecture, development, optimization, and operation of production LLM and AI automation systems. Design capabilities including agents, retrieval, code generation, tool use, model routing, evaluation, and observability. Drive platform modernization, scalability, reliability, architecture, and code quality while mentoring engineers and influencing technical practices across teams. Build prototypes, establish engineering standards, conduct reviews, and communicate complex technical concepts through documentation and cross-functional collaboration.

Lead the design and development of production AI systems for enterprise application automation. Responsibilities include building reliable agents and orchestration, extracting specifications from existing systems, modeling application intelligence and retrieval, developing evaluation and CI pipelines, optimizing reliability and scale, using AI coding tools, mentoring engineers, and documenting technical practices.

Build and optimize developer-facing tools, including IDE integrations, CLIs, code-analysis systems, and CI/CD workflows. Integrate transparent, reliable AI capabilities into developer experiences, optimize high-volume production services, and improve availability, latency, and efficiency. Drive engineering best practices through code reviews, automation, documentation, and technical leadership while mentoring junior engineers. The role also requires communicating complex architectures and operating tooling across enterprise deployment environments.

Own the evaluation, selection, optimization, and production deployment of large language models powering legal and medical document workflows. Build EvalOps and monitoring frameworks, benchmark model quality, cost, speed, and resilience, manage model transitions, optimize RAG and orchestration pipelines, and reduce hallucinations. Partner with product and go-to-market stakeholders while independently implementing changes across staging and production.

Build and harden Nava’s shared AI platform for LLM-based products. Responsibilities include reusable context and agent-state management, safe long-running agent execution, sandbox integration, model routing, subagent orchestration, token-cost optimization, tracing, evaluation, testing, and documentation. The role partners with product teams to create maintainable infrastructure that supports production AI features at scale.

Develop and implement AI-driven applications, workflows, and integrations on the ServiceNow platform. Gather requirements, configure and customize ServiceNow modules, integrate machine learning and NLP capabilities, develop scripts, manage CMDB and service mapping work, test solutions, troubleshoot incidents, and support deployments across environments. The role also involves stakeholder collaboration, documentation, production remediation, and applying ServiceNow AI features such as Now Assist, Virtual Agent, Predictive Intelligence, and Performance Analytics.

Engineer AI-powered threat detections, automated response playbooks, and continuous adversary emulation. Apply machine learning and LLMs to security operations, conduct threat hunting and AI red teaming, map coverage to MITRE ATT&CK, and develop detection-as-code through CI/CD. Build production-grade Python integrations, measurable containment capabilities, and safe autonomous defenses with guardrails. Mentor security professionals and collaborate across threat response, cyber defense engineering, and platform teams.

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.

Develop production AI applications using LLMs, RAG architectures, agentic AI, and multi-agent systems. Build Python APIs and backend services, implement vector search and embeddings, and deploy solutions across Azure AI services. Collaborate with architects, product teams, and stakeholders on enterprise AI projects, architecture decisions, best practices, and technical standards. The role requires strong Python expertise, Azure AI experience, modern AI orchestration frameworks, and CI/CD knowledge.

Build and productionize agentic workflows, RAG pipelines, AI services, backend systems, and ML pipelines for sales automation. Integrate Salesforce, quoting engines, data lakes, and internal platforms using scalable, event-driven architectures. Lead 0-to-1 development of tools for account research, lead prioritization, and quoting while ensuring reliability, observability, performance, and resilience across the GTM technology stack.

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.

Leads Wasabi’s AI infrastructure, data factory, and developer partnerships strategy. Hunts and originates new relationships, develops joint go-to-market and co-selling motions, structures revenue-generating deals, and manages executive partner relationships. The role collaborates with Business Development, Sales, Marketing, and Product, builds the developer ecosystem, tracks pipeline and revenue performance, and represents Wasabi at industry events. Candidates need deep AI infrastructure and storage expertise, an established industry network, and a proven record of closing strategic partnerships.

Build reference software codebases, support NGOs developing generative AI products, integrate diverse data sources, create experimentation tools, and deliver rapid cloud-based solutions. The role includes advising and mentoring partner organizations, conducting rigorous experiments, improving analytics infrastructure, organizing technical workshops, and visiting NGO sites to identify technical needs. Candidates need strong full-stack engineering, backend, data engineering, cloud, database, and generative AI experience.

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.

Lead the architecture, development, and deployment of scalable AI-powered backend systems. Design APIs and inference workflows, fine-tune and productionize LLM and transformer models, establish MLOps and governance standards, and evaluate AI technologies. Mentor engineers, guide cross-functional technical initiatives, improve system reliability and performance, and shape the organization’s AI strategy and roadmap.

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.

Design, develop, deploy, and optimize Generative AI applications using LLMs and AI frameworks. Build scalable APIs, automate workflows, integrate foundation models with enterprise systems, and improve model performance through testing and experimentation. Implement monitoring, quality assurance, governance, security, compliance, documentation, and risk mitigation. Evaluate emerging technologies, support proofs of concept, and collaborate with product, engineering, and business teams.

Leads the architecture, development, deployment, governance, and optimization of enterprise Generative AI solutions. Builds applications using LLMs and foundation models, develops prompt engineering frameworks, supports AI strategy, ensures secure and responsible deployment, mentors engineers, evaluates emerging technologies, and translates business needs into scalable technical architectures.

Build and optimize production-grade agentic AI systems for marketing and enterprise customer experience use cases. Responsibilities include agent orchestration, tool use, RAG, embeddings, guardrails, LLM evaluation, observability, performance optimization, and scalable Python services deployed on AWS. The role partners across product and engineering teams and requires hands-on experience with LLM agents, marketing data platforms, retrieval systems, and AI evaluation.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.