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

Build and maintain AI-powered go-to-market systems that automate sales, expansion, marketing, forecasting, handoffs, and post-sales workflows. Optimize processes, maintain CRM data quality, test and monitor automations, connect GTM tools, and translate operational needs into scalable technical solutions. The role uses AI and iPaaS tools to surface next-best actions, improve deal velocity, and support customer lifecycle operations.

Design and implement AI agents, plugins, automations, and enterprise integrations for HR Technology platforms. Lead requirements gathering, technical analysis, solution design, testing, documentation, deployment, security, and data integrity efforts across HR, IT, and business stakeholders. Integrate platforms including ServiceNow, Workday, and other enterprise applications while supporting the HR automation and AI roadmap.

Build and own evaluation infrastructure, observability tooling, datasets, annotation workflows, and diagnostic systems for production AI agents. Lead experiments across prompts, models, routing, caching, cost, and latency. Diagnose failures across planning, tool use, and agent execution, while partnering with AI Core engineering. Manage and grow the AI Quality team as a hands-on player-coach, contributing directly to production code and technical direction.

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.

Supports AI engineering initiatives at Vantor, contributing to spatial intelligence solutions and helping develop technologies that enable customers to understand current and future conditions. The posting provides limited role-specific responsibilities but emphasizes problem-solving, innovation, collaboration, and mission-focused work. Applicants must qualify as a U.S. Person under applicable export control and ITAR requirements.

Own application security for Generative AI and emerging products by integrating secure development practices, conducting design and code reviews, threat modeling systems, assessing vendors and AI tools, triaging vulnerabilities, and building security automation. The role partners with engineering and detection teams, contributes to cloud and infrastructure security, participates in incident response and on-call rotations, and documents post-incident remediation.

Implements and supports enterprise AI infrastructure platforms across client environments. Configures Kubernetes, workload schedulers, GPU resource governance, containers, observability, security, and related platform services. Performs discovery, deployment, integration, upgrades, troubleshooting, validation, documentation, and knowledge transfer. Collaborates with cross-functional technical teams, contributes to implementation plans and Statements of Work, manages project deliverables, and provides client-facing consulting support. Travel and occasional extended or weekend work may be required.

Build and operate Affirm’s AI platform, including LLM and MCP gateways, agent runtimes, evaluation services, and governance controls. Design highly available backend systems, manage identity, quotas, routing, caching, reliability, observability, and security for AI workloads. Own projects from design through launch and on-call, establish engineering standards, collaborate across teams, mentor engineers, and help scale AI systems into production.

Build and operate production-grade AI developer tooling, agentic workflows, and shared infrastructure that improve software development across engineering teams. Responsibilities include AI-assisted code review, testing, issue triage, model integrations, evaluations, monitoring, cost controls, adoption enablement, and cross-functional collaboration with security, platform, quality, product, and design teams.

Leads the architecture, roadmap, and delivery of AI/ML and LLM solutions for defense and intelligence missions. Develops projects from proof of concept through production, supports customer demonstrations, collaborates on bids and proposals, and guides multidisciplinary teams. The role requires cloud-native and distributed-systems expertise, stakeholder engagement, innovation, and experience with edge AI, autonomous systems, intelligence analysis, logistics, or readiness programs. Remote work is available with up to 25% travel and occasional on-site requirements.

Build Agent OS platform services for production-grade AI agents, including runtime execution, typed tools, context and memory systems, approval workflows, evaluation infrastructure, observability, and model routing. Develop reliable APIs and workflows, debug agent failures, design regression suites, establish platform contracts, and partner with product, security, architecture, data, and domain engineering teams.

Build core platform infrastructure for production AI agents, including runtime execution, typed tools, context and memory systems, approval workflows, evaluation, observability, and model routing. Develop reliable APIs and services, debug agent failures, design regression suites, and partner with product and engineering teams to create reusable, secure, multi-tenant agent capabilities.

Designs, develops, and deploys enterprise Generative AI and LLM applications. Builds RAG architectures, agentic AI workflows, vector search and retrieval pipelines, REST API integrations, and cloud-native Azure services. Works with stakeholders and engineering teams to deliver secure, scalable solutions, supporting testing, deployment, monitoring, troubleshooting, and optimization. Requires strong Python, AI application development, Azure, search, API, and software engineering skills.

Build and maintain AI-powered workflows, agents, automations, internal tools, and production systems for creative ideation, copy generation, video pipelines, reporting, and client delivery. Integrate LLMs and third-party APIs, ship rapidly, iterate based on real usage, and develop practical tools that improve marketing operations.

Own the technical design and delivery of multi-component AI systems, including LLM-backed features, agent tool orchestration, evaluation frameworks, data pipelines, and model harnesses. Build reliable APIs, SDKs, and feedback loops; optimize latency and cost; assess external models and datasets; and support production systems through on-call rotation. Mentor engineers and collaborate across search, location intelligence, and platform teams.

Develops and evaluates AI-powered software, automation scripts, prototypes, and enterprise solutions using programming languages, LLMs, APIs, and AI-assisted coding tools. Collaborates with engineers, architects, and business development teams on technical research, federal proposals, workflow automation, and solution documentation. The role also assesses AI-generated code for quality, security, performance, and maintainability while supporting adoption of approved AI technologies.

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.

Leads hands-on development of production agentic AI capabilities for Humana’s NBA platform. Designs multi-step AI workflows, integrates LLMs, builds RAG and vector-search systems, develops user-facing AI experiences, and establishes evaluation and reliability frameworks for regulated healthcare use. Leads a small engineering team and contractors, partners with product and frontend teams, and promotes AI-assisted development 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.

Design, build, deploy, and optimize production-grade machine learning and LLM systems, including RAG pipelines, agentic workflows, fine-tuning, embeddings, and chatbots. Own projects from architecture and prototyping through deployment, monitoring, and iteration. Collaborate directly with enterprise clients, mentor junior ML engineers, conduct code reviews, and help establish internal technical standards and best practices.

Develop and deploy AI models using Python and Azure, advance Agentic AI applications, optimize data workflows, and validate model reliability and scalability. Collaborate with Agile teams to deliver project solutions, provide technical expertise, and stay current with AI and cloud technology advancements.

Design, build, deploy, and maintain production AI/ML systems, including models, data pipelines, training workflows, agentic systems, and RAG pipelines. Monitor and optimize reliability, scalability, performance, and costs while contributing to architecture decisions. Collaborate with product, engineering, and data teams, communicate technical tradeoffs, support junior engineers, and apply responsible AI, security, observability, and containerization practices.