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

Lead the design and implementation of AI initiatives for edge devices. Develop and optimize machine learning solutions using large-scale sensor, video, text, diagnostic, transaction, and location data. Partner with engineering and business teams to prototype AI experiences, ship production systems, adopt research advances, and improve edge model performance. Mentor ML engineers and communicate technical concepts to varied audiences.

Build and deploy generative AI solutions, including APIs, MCP servers, AI agents, LLM integrations, and full-stack applications. Develop scalable AWS cloud services, observability systems, evaluation pipelines, and production agent workflows. Collaborate with Product, UX, Security, Legal, and engineering teams to design interfaces, adapt AI tools to company codebases, and support customer experience and developer productivity use cases.

Build and improve the Enterprise AI operating model through AI-enabled processes, operational reporting, knowledge management, stakeholder forums, intake governance, and asset registry oversight. The role translates complex program information into executive narratives, coordinates decision-making, enables cross-functional alignment, and continuously improves planning, prioritization, governance, communication, and execution mechanisms.

Lead managed services for clients' Azure, Databricks, and Microsoft Fabric data environments. Architect, implement, monitor, troubleshoot, and govern data platforms, ETL pipelines, data warehouses, semantic models, reporting, and AI/ML solutions. Manage incidents and SLAs, advise clients on licensing and technical strategy, communicate solutions, document environments, and mentor distributed engineering teams.

Build and own ClickUp’s AI evaluation systems, agent harness, self-improvement loops, model benchmarking, routing, and fallback strategies. Architect scalable backend and AI platform services for LLM deployment, orchestration, monitoring, logging, and automated evaluation. Improve reliability, privacy, security, and compliance while integrating AI features into the core product and collaborating cross-functionally.

Build and deploy a secure, offline document-intelligence system for RFQ screening. Responsibilities include PDF and drawing processing, OCR, multimodal model deployment, structured citation-grounded extraction, validation rules, audit logging, evaluation frameworks, and local versus Azure OpenAI comparisons. The role also includes packaging signed offline releases, documenting deployments, supporting onsite stakeholder sessions, and assisting client IT teams within strict CUI and air-gapped security boundaries.

Build and operate production LLM systems using Ollama and llama.cpp, develop RAG pipelines and MCP integrations, and create Python backend services for AI features. Deploy and maintain workloads across Azure and AWS using Docker and CI/CD. Automate operations, process structured data, support Kafka-based architectures, troubleshoot across the stack, and progressively develop expertise in model quantization, GPU tuning, and secure offline deployments.

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.

The AI Engineer designs, develops, and operationalizes responsible AI systems, including generative AI applications, RLHF models, guardrails, prompt engineering, and RAG frameworks. Responsibilities include evaluating and fine-tuning models, developing Python applications on cloud platforms, implementing ML software engineering practices and CI/CD pipelines, and collaborating with stakeholders on AI roadmaps and delivery. The role requires leading complex AI/ML initiatives and translating technical concepts for diverse audiences.

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.

Own production agentic AI systems that audit medical claims. Translate ambiguous business problems into measurable technical initiatives, define evaluation metrics, architect agent systems, build reliable Python software, and manage accuracy, latency, cost, and citation grounding. Lead technical direction, review designs, mentor engineers, collaborate with clinical and business stakeholders, and present defensible results. The role requires hands-on experience with agent SDKs, evaluation harnesses, long-context systems, and AI development tools.

Build and operate production AI capabilities across enterprise platforms, including agent runtimes, model routing, RAG, tool calling, guardrails, evaluation, monitoring, and cost and latency optimization. Develop reliable, measurable, safe, and cost-conscious AI systems; create evaluation frameworks and quality controls; monitor production behavior; and collaborate with backend, platform, product, security, and client teams to deliver practical AI solutions.

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.

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.

Build, test, deploy, and productionalize secure AI and machine learning systems for internal tools, customer products, and operational workflows. The role involves generative AI, NLP, predictive modeling, responsible AI, model evaluation, monitoring, documentation, and collaboration with engineering, data, and business teams. Candidates should have strong Python and software engineering skills, experience with LLM applications and modern AI tooling, and familiarity with cloud platforms, privacy, security, and compliance.

Design, build, and operate production multi-agent AI systems and automation for Marketing Operations. Integrate LLMs with business data (BigQuery, CRMs, Slack), build MCP servers, APIs, and scalable serverless/containerized services, implement observability and governance, and partner with GTM teams to deliver measurable automation and self-service workflows.

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 ship production AI features for an insurance compliance platform. Responsibilities include developing LLM pipelines, retrieval and agent integrations, document data extraction, evaluation systems, automated quality checks, and AI-powered assistants. The role uses Python/FastAPI, React/TypeScript, and PostgreSQL while emphasizing security, reliability, maintainability, and rapid prototyping. The engineer will own features end to end, investigate complex customer documents and edge cases, and communicate AI tradeoffs to business and product partners.

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.

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.

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.