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

Lead Samsara’s AI initiatives across the software development lifecycle, including developer agents and SRE AI. Set strategy, select experiments, make build-versus-buy decisions, control infrastructure and token costs, scale automated incident response, and build trust in AI-driven actions. Manage and grow a team of five to ten senior engineers while partnering directly with R&D leaders and developers.

Designs and delivers production AI agents, assistants, and automations across CRM and revenue systems. Owns Salesforce architecture, including Apex, Lightning Web Components, Flow, CPQ, security, integrations, and data models. Leads AI evaluation, cost management, monitoring, and ROI measurement. Integrates Salesforce with SAP, DocuSign, HubSpot, Snowflake, and AWS services. Manages DevOps releases, technical standards, Agile projects, user acceptance testing, adoption, and engineering mentorship.

Build agentic AI products that enable customers to create custom detections and workflows from operational camera data. Own projects from customer discovery through design, implementation, production operation, and ongoing optimization. Develop backend services, APIs, and customer-facing experiences; partner with platform, machine learning, product, and design teams. Improve AI accuracy, reliability, performance, and cost while mentoring engineers and shaping technical architecture.

Architect and build Toast’s Vision AI platform, including video ingestion, processing, storage, inference pipelines, evaluation, and monitoring. Develop production ML services and customer-facing computer vision features, make architecture decisions, write significant code, and guide technical direction. Partner across teams, mentor engineers, establish reliability, security, and privacy standards, and promote AI-assisted development. The role requires deep distributed-systems and cloud expertise, production ML experience, video familiarity, and the ability to take ambiguous ideas from prototype to customer-ready products.

Build and own production Vision AI systems end to end, including model selection, inference pipelines, evaluation, monitoring, and customer-facing features. Shape architecture, collaborate with product, design, and engineering teams, and use AI coding tools to accelerate development. Establish high standards for quality, testing, reliability, security, and privacy while mentoring engineers and taking ambiguous ideas from prototype to production.

Apply established machine learning techniques—including clustering, classification, and anomaly detection—to ICS/OT cybersecurity data. Integrate model outputs into batch and near-real-time pipelines, evaluate third-party models, maintain reliable data contracts and observability, and troubleshoot production issues such as data drift and output quality. Collaborate with AI engineers, data engineers, and product teams while clearly communicating model behavior, uncertainty, and downstream use.

Designs, develops, integrates, tests, and deploys AI/ML applications and MLOps solutions for clients. Builds model-serving workflows, APIs, RAG solutions, enterprise integrations, and containerized NVIDIA AI services. Troubleshoots software, model, data, API, and platform issues; creates technical documentation and client handoff materials; contributes to project estimates and implementation plans. This client-facing role also manages scope, schedule, budget, deliverables, and occasional travel to client sites.

Build full-stack Vision AI products for restaurant operators, including backend APIs, video pipelines, AI model integrations, monitoring, and customer-facing interfaces. Own platform components, contribute to architecture, collaborate across product and engineering teams, and use AI-assisted development tools. The role requires production software experience, cloud infrastructure expertise, event-driven systems knowledge, and comfort working across backend, frontend, data pipelines, and machine learning integrations.

Design, deploy, and operate security controls for Dropbox’s AI and agentic infrastructure across cloud, Kubernetes, data stores, and on-premise systems. Secure authentication, authorization, networking, AI tools, and non-human workloads while addressing prompt injection, poisoning, supply-chain, and MCP risks. Lead security initiatives, harden infrastructure, automate policy and security operations, and collaborate cross-functionally to protect customer data. Participate in on-call rotations as required.

Develop AI-powered applications and prototypes using LLMs, prompt engineering, RAG, embeddings, vector databases, and AI APIs. Support data preprocessing, model evaluation, API integration, and research into emerging AI technologies. Collaborate with software and AI engineers to integrate generative AI features into web and backend applications while building practical skills in Python, machine learning, and modern LLM frameworks.

Designs and implements conversational AI solutions using Dialogflow CX, CCAI technologies, cloud platforms, and telephony or CRM integrations. Leads client architecture, requirements gathering, conversational flow design, technical documentation, governance, and solution delivery. Collaborates with executives, architects, engineers, and business stakeholders, while mentoring team members and evaluating emerging generative AI capabilities. The role is remote and requires up to 10% travel.

Build and operate backend services, asynchronous workflows, and asset pipelines supporting conversational AI experiences. Manage versioned AI configurations, prompts, knowledge artifacts, and media assets; integrate shared services and external APIs; implement security, observability, retries, testing, release, and rollback processes. Evaluate compatibility, privacy, licensing, reliability, storage, processing time, and vendor costs while collaborating with engineering teams and digital-human specialists.

Build and deploy autonomous AI agents that support enterprise workflows and customer experiences. Responsibilities include integrating agents with REST APIs, microservices, and cloud platforms; optimizing multi-agent systems for scalability, performance, and security; collaborating with product managers and data scientists; and exploring generative AI, LLM orchestration, and multi-agent frameworks. The role also involves LLM inference optimization and building language models using LoRA and QLoRA techniques.

Develop and test AI-powered product features for a healthcare software platform. Responsibilities include integrating large language models and APIs, improving prompts, validating structured outputs, building backend and frontend integrations, writing automated tests, evaluating model responses, troubleshooting issues, documenting implementations, and collaborating with engineers and product teams. The role emphasizes production-quality software, responsible AI practices, privacy, reliability, and learning modern AI development tools.

Lead the architecture and development of an enterprise workflow automation platform enhanced with generative AI. Build APIs, integrations, workflow engines, and custom customer solutions; deploy virtually or on-site across the United States; conduct workflow discovery; and serve as the technical customer-facing lead. The role requires autonomous technical decision-making, rapid iteration, leadership of engineers, and strong communication with executives and enterprise clients.

Lead the architecture and development of an enterprise workflow automation platform enhanced with generative AI. Build APIs, integrations, workflow engines, and platform foundations, then deploy solutions virtually or onsite with enterprise customers across the US. Conduct workflow discovery, rapidly prototype custom solutions, make independent technical decisions, guide engineers, and serve as a technical customer-facing leader.

Designs, develops, deploys, and optimizes machine learning models and AI solutions for enterprise automation, analytics, and decision-making. Analyzes datasets, improves data pipelines, collaborates with cloud and engineering teams, supports AI-driven workflows, contributes to solution design, and documents technical processes. The role requires Python, machine learning frameworks, cloud platform experience, SQL, APIs, and knowledge of MLOps and AI deployment pipelines.

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.

Own the design, development, integration, release, and reliability of tailored AI solutions for enterprise clients. Adapt a core AI platform, deliver client-specific changes to a shared codebase, manage production rollouts, diagnose complex issues, and serve as the primary technical contact for client engineers. The role requires strong Python, software architecture, system design, and experience deploying LLM or agent-based systems used by customers in live environments.

Build and operate production AI agent systems that extract and validate data from documents and spreadsheets, integrate with external systems, manage workflow state and failures, and support human review. Develop evaluation datasets, regression tests, monitoring, and reliability improvements while balancing model use with deterministic code. Collaborate with retirement plan experts, lead technical design, write documentation, review code, and mentor engineers.

The posting provides no specific responsibilities or qualifications. The role title indicates a Full Stack AI Engineer position using .NET.

Designs, develops, and supports digital tools, automated workflows, dashboards, data integrations, and AI-enabled applications for legal services and law firm operations. Responsibilities include requirements gathering, solution delivery, Power Platform development, AI agent configuration, testing, documentation, project management, stakeholder engagement, training, adoption, and responsible evaluation of emerging technologies. The role requires collaboration with attorneys, business teams, data specialists, vendors, and governance stakeholders while protecting confidential and client information.

Build and operate secure, scalable production infrastructure for agentic AI platforms across AWS and Azure. Responsibilities include CI/CD automation, infrastructure as code, cloud security, governance, monitoring, observability, model lifecycle management, drift detection, incident response, networking, resilience, and cost optimization. The role establishes production-readiness standards, supports AI deployments, and partners with engineering, product, security, compliance, and business teams.

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.