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

Leads the design, development, and delivery of production AI systems, including agentic workflows, RAG architectures, backend services, evaluations, and orchestration infrastructure. Provides hands-on technical leadership, architectural direction, mentorship, code reviews, and engineering standards improvements. Partners with product and engineering stakeholders to translate ambiguous requirements into reliable, observable, scalable, and maintainable AI solutions.

Designs and deploys AI-powered features and scalable AI/ML services across SaaS products. Responsibilities include translating business problems into AI solutions, integrating large language models, preparing data, evaluating model performance, writing Python, building secure deployment infrastructure, and collaborating with product, data, and cloud operations teams. The role requires production AI/ML experience, scalable data infrastructure expertise, AWS familiarity, and modern DevOps practices.

Designs, implements, validates, benchmarks, troubleshoots, and supports accelerated compute and high-performance storage infrastructure for enterprise AI workloads. Conducts discovery, migrations, installations, configuration, testing, documentation, and client knowledge transfer. Collaborates with project managers, architects, sales teams, partners, and stakeholders to manage scope, timelines, risks, and deliverables. The role requires client-facing consulting, technical documentation, infrastructure troubleshooting, occasional overtime, and travel to client sites or company locations.

Implements, validates, troubleshoots, and optimizes high-performance AI network fabrics supporting GPU clusters and distributed workloads. Configures InfiniBand, Ethernet, RoCE, routing, congestion management, telemetry, cabling, and fabric-management systems. Performs installation, burn-in, performance testing, documentation, knowledge transfer, and client handoffs. Contributes to statements of work, implementation plans, and solution validation while managing project scope, schedules, budgets, and customer communication.

Own Cerby’s AI agents for API catalog generation, browser workflow creation, and self-healing automation. Take existing systems into production using durable workflows, observability, evaluation benchmarks, cost controls, and security protections. Responsibilities include detecting and repairing automation drift, managing credential isolation and prompt-injection risks, defining threat models, partnering with engineering teams, writing design documentation, mentoring contributors, and providing on-call ownership after launch.

Build and deploy internal AI tools, agents, integrations, and workflow automations. Develop scalable AI infrastructure, connect LLMs with enterprise systems, and establish secure, reliable production practices. Partner with teams to identify opportunities, translate business needs into technical solutions, and optimize model performance, usage, costs, and business impact. Evaluate emerging AI technologies and create reusable frameworks that accelerate company-wide AI adoption.

Leads architecture and delivery of enterprise generative AI platforms and LLM-powered systems, including RAG, agentic workflows, model integrations, evaluation tooling, and governance. Owns platform architecture, security, privacy, observability, cost attribution, and responsible AI practices in a regulated healthcare environment. Mentors AI engineers, establishes technical standards, leads design reviews, evaluates vendors and models, and partners with engineering, product, legal, compliance, and privacy teams.

Designs, deploys, and scales enterprise AI agents, RAG systems, automations, and integrations across ServiceNow and Microsoft ecosystems. Builds solutions with Claude, Copilot Studio, Azure AI Foundry, Azure AI Search, and related orchestration tools. Responsibilities include full-stack development, customer-facing implementation, solution architecture, evaluation, responsible AI controls, troubleshooting, and reusable platform development. The role requires extensive travel and collaboration with technical and business stakeholders.

Design and build Python APIs, agentic AI frameworks, LLM-powered clinical documentation systems, and EHR integrations. Evaluate and improve AI output quality for real-world clinical workflows, collaborate with physicians and customers to resolve documentation issues, and contribute across the platform’s full stack. The role requires strong Python, LLM, API, testing, and communication skills, with Google Cloud Platform and Firebase experience preferred.

Leads the design, development, deployment, and operation of scalable AI solutions, including generative AI, agentic workflows, RAG pipelines, MCP integrations, workflow automations, and evaluation systems. Builds production software across a polyglot technology stack, establishes testing and observability practices, mentors engineers, influences technical direction, and partners cross-functionally to deliver secure, maintainable, responsible AI applications.

Designs, deploys, and maintains AI and machine learning solutions, including generative AI, LLM, RAG, and conversational AI applications. Owns agentic engineering infrastructure, application guardrails for patient-care workflows, data pipelines, analytics, model governance, monitoring, and cloud deployment. Provides architectural leadership, improves engineering throughput, ensures privacy and compliance, and translates business needs into scalable AI solutions.

Design and optimize prompts, agent architectures, MCP tools, RAG workflows, and context-management strategies for reliable AI applications. Develop and integrate LLM-powered solutions with enterprise systems, APIs, healthcare data, and backend services. Establish evaluation and testing frameworks, improve response quality, and support deployment, monitoring, troubleshooting, security, compliance, governance, and responsible AI practices.

Build production-ready agentic AI capabilities focused on monitoring, guardrails, evaluation, security, quality, and human-agent collaboration. Perform machine learning tasks including exploratory data analysis, feature engineering, and model fine-tuning. Translate research into scalable software, rapidly prototype features, collaborate cross-functionally, and explore emerging agentic technologies.

Build and deploy enterprise AI solutions using Claude, LLMs, RAG, agentic workflows, tool use, and structured outputs. Integrate AI applications with ServiceNow and enterprise systems, develop secure APIs and supporting services, evaluate model quality, implement responsible AI safeguards, troubleshoot production workflows, and collaborate directly with customers. The role also contributes reusable accelerators, platform capabilities, documentation, and deployment best practices while supporting discovery, prototyping, implementation, and production rollouts.

Deploys enterprise AI and agentic workflows for customers using Claude, LLM integrations, RAG, tool use, structured outputs, and responsible AI controls. Builds full-stack integrations across ServiceNow and enterprise systems, develops prototypes and production solutions, evaluates AI quality and safety, troubleshoots deployments, and contributes reusable components to NewRocket’s AI platforms. The role is highly customer-facing and requires approximately 70% travel.

Deploy and operationalize enterprise AI workflows, agents, RAG systems, and integrations across ServiceNow and connected platforms. Build full-stack components, LLM applications, APIs, prototypes, and production solutions using Claude and other AI technologies. Collaborate with customers and internal teams on discovery, implementation, troubleshooting, evaluation, security, responsible AI, and continuous improvement. Develop reusable accelerators, platform capabilities, integration patterns, and deployment practices while traveling extensively to support customer engagements.

Build and deploy secure, production-ready generative AI solutions for enterprise customers. Responsibilities include developing Claude-powered agents, RAG systems, workflow automations, APIs, integrations, and full-stack components; conducting evaluations; implementing responsible AI controls; troubleshooting deployments; and collaborating with customers and internal teams. The role also contributes reusable accelerators and platforms while requiring substantial customer engagement and travel.

Own and improve ClickUp’s voice AI systems, including real-time speech-to-text, audio processing, context-aware transcription, LLM post-processing, mention resolution, formatting, and voice-to-action workflows. Evaluate and integrate ASR models for accuracy, latency, and cost, while collaborating across product and platform teams to deliver voice and multimodal assistant features across desktop, mobile, web, and browser extension products.

Build and operate production AI capabilities using Python, LLMs, retrieval, and intelligent automation. Responsibilities include integrating LLM APIs, developing retrieval-augmented features, creating evaluation and prompt frameworks, applying security and compliance controls, monitoring production systems, and collaborating across engineering, data, security, product, and operations teams.

Designs, builds, deploys, evaluates, and optimizes production AI systems, including RAG pipelines, agentic workflows, and model integrations. Uses Python and cloud platforms to deliver scalable solutions, establishes quality and monitoring frameworks, addresses responsible AI and security concerns, and manages cost-performance tradeoffs. Serves as a senior technical client advisor, supports discovery and presales, mentors engineers, researches emerging AI technologies, and develops reusable accelerators.

Designs and deploys enterprise AI applications and Copilot Studio agents using Azure AI, Azure OpenAI, Python, and Power Platform. Builds RAG solutions, APIs, workflows, and responsible AI safeguards; integrates Microsoft 365 and enterprise data. Supports proofs of concept through production, establishes engineering and deployment practices, tracks solution impact, and guides makers and stakeholders on AI adoption.

Lead the technical direction, development, evaluation, security, observability, and cost optimization of production LLM agent systems. Build shared agent infrastructure, improve reliability and fault tolerance, establish testing and rollout standards, and partner with engineering, product, research, and security teams. Mentor engineers, support AI coding-agent adoption, investigate incidents, and guide model selection and production readiness across teams.

Build and operate production-grade agentic AI systems for enterprise SaaS, including orchestration, MCP tools, memory, RAG, knowledge retrieval, security guardrails, evaluation, and observability. Own products from prototyping through production, make tradeoffs among accuracy, latency, reliability, and cost, support incident response, create reusable tooling, and mentor engineers. The role requires expert Python, cloud-native infrastructure, distributed systems, and hands-on LLM production experience.

Builds infrastructure, evaluation systems, tooling, and workflows for AI-powered software development. The role focuses on background agents, agent performance measurement, evaluation sets, feedback loops, developer adoption, code review improvements, reliability, observability, and safe autonomous engineering workflows. It requires hands-on software engineering, technical leadership, product judgment, collaboration with internal users, and the ability to deliver ambiguous, dependency-heavy projects.

Build autonomous AI agents that translate proven attack paths into targeted security-control changes, safely apply or stage those changes, and verify remediation by rerunning attacks. Responsibilities include ML reasoning systems, evaluation and training data pipelines, counterfactual experiments, heterogeneous control-plane integrations, safety architecture, staged rollouts, rollback, auditability, and diagnosing agent failure modes. The role requires production ML engineering, agentic systems or LLM post-training, outcome-based evaluation, Python software engineering, distributed systems, and cloud infrastructure experience.