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

Develop machine learning models, predictive solutions, data pipelines, data models, and feature engineering workflows. Analyze large datasets, validate model outputs, document assumptions, and translate business needs into technical requirements and recommendations. Collaborate with stakeholders, refine analytical use cases, support solution prototyping, and guide junior team members through coaching and technical feedback.

Build and deploy AI-native products that transform large-scale geospatial data into actionable intelligence. Design multi-agent workflows, integrate frontier LLMs, engineer tool-using agents, and develop data pipelines for anomaly detection and predictive analytics. Ensure secure, scalable cloud integrations and operate production AI systems using DevOps, containerization, orchestration, and CI/CD. Establish feedback and reinforcement mechanisms to improve model reliability while contributing to AI-first engineering standards and documentation.

Design and deploy production AI assistants, copilots, agents, RAG systems, and workflow automation using enterprise data. Build AI backend services, APIs, microservices, and AWS serverless workflows; integrate enterprise platforms; implement observability, responsible AI, human-in-the-loop controls, and traceability. Partner with Data, IT, and business teams to improve operational processes and decision-making while managing production incidents, model rollbacks, performance, accuracy, and cost.

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.

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.

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.

Build and manage AI-powered workflows, agents, automations, and internal applications for Deal Desk and GTM teams. Identify automation opportunities, develop prototypes, scale solutions to production, integrate enterprise data and APIs, implement human validation and audit controls, optimize LLM costs, document tools, measure adoption, and train users. The role requires Deal Desk or RevOps experience plus practical knowledge of AI models, APIs, scripting, SQL/SOQL, Python, CPQ, CLM, Salesforce, and agentic platforms.

Owns enterprise AI security assessment, architecture, and control programs. The role evaluates AI systems, vendors, platforms, and shadow AI; designs secure architectures, guardrails, identity models, sandboxes, gateways, and runtime controls; and develops detection and response capabilities for prompt injection, jailbreaks, data leakage, and tool misuse. The engineer partners with governance, identity, platform, engineering, and security operations teams, advises senior leaders, drives remediation, and mentors security professionals.

Design, build, deploy, and scale production AI agents and platforms for legal workflows. Develop and evaluate agent solutions, optimize performance and cost, redesign business processes, and promote responsible AI adoption. Collaborate with Legal, Enterprise AI, Data, Security, Privacy, and Engineering teams to deliver secure, reliable, scalable solutions. Contribute frameworks, training, governance guidance, and innovative applications using modern LLM and AI techniques.

Lead AI Engineer defines AI strategy, architecture, and standards; makes build vs. buy decisions; architects scalable, reliable LLM/GenAI systems; establishes LLMOps, evaluation, guardrails, and observability; coordinates cross-functional alignment with product and engineering; and champions AI-assisted developer tooling and responsible AI practices.

Build and own conversational voice AI products end-to-end: architect, implement, and deploy the platform. Design infrastructure, develop front-end and back-end systems, deliver reliable customer outcomes, and collaborate directly with the CTO to scale the product for enterprise customers.

Leads the design, development, deployment, and optimization of AI-powered marketing workflows. Owns the Marketing AI and Automation backlog, designs LLM, RAG, and multi-agent systems, builds data foundations, and operationalizes secure, observable production solutions. Partners with marketing, Martech, analytics, and leadership teams to define use cases, metrics, integrations, guardrails, and enablement strategies.

Build and deploy reliable, measurable, and safe AI agents for real business workflows. Work within Marketing Engineering and growth pods to identify problems, develop end-to-end solutions, evaluate LLM performance, and measure business impact. The role involves productionizing agent systems beyond demos, using Python, SQL, LLM APIs, and orchestration tools, with potential integration into go-to-market data and marketing systems.

Build production AI systems for finance teams, including customer-specific anomaly-detection models, grounded natural-language generation, agentic workflows, and managed LLM integrations. Develop Python services with warehouse access and customer-hosted deployments. Implement verification, human-confirmation checkpoints, anti-leakage controls, and CI evaluation for grounding and hallucination. Use customer feedback to improve models and systems while collaborating with AI and evaluation engineers.

Own AI workflows end to end, from system design and model selection through production deployment, evaluation, monitoring, and continuous improvement. Build reliable agentic and LLM-powered applications with structured outputs, tool use, human review, fallbacks, observability, and audit trails. Integrate AI services with backend systems while managing cost, latency, security, and customer data isolation. Establish engineering standards, mentor peers, and help teams use AI coding tools effectively.

Fine-tune and align LLMs for specialized financial tasks; build agentic solutions with LangChain/Autogen; engineer ML pipelines for large financial datasets; deploy, monitor and scale production models; track AI research and integrate emerging tooling.

Design, build, and operate production AI automations and agentic workflows for Toast’s customer care systems. Integrate LLMs with Salesforce Service Cloud, Amazon Connect, and workflow platforms; develop autonomous actions with human oversight; test, monitor, and improve system reliability, accuracy, latency, and cost. Collaborate with software engineers, Salesforce developers, and care operations stakeholders to translate workflows into scalable intelligent solutions.

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 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.

Lead design and build of an AI-native marketing platform: integrations, multi-agent orchestration, UIs, tooling, analytics, CI/CD, auth/multi-tenancy, and production AI agents to automate marketing workflows and drive adoption across teams.

Design and deploy production-ready LLM pipelines and agentic workflows, automate large-scale multimodal dataset pipelines, monitor and improve agent performance, and integrate AI agents with 3D scene composition. The role also involves process documentation, incident response, and continuous improvement, with collaboration across the engine team and company founders.

Build and ship production AI products, including LLM coaching experiences, agentic workflows, retrieval and orchestration systems. Develop predictive, classification, ranking, and recommendation models; establish evaluation and quality processes; create trusted data pipelines and knowledge sources; and integrate AI with enterprise systems. The role requires strong ownership, safety-conscious engineering, evidence-based recommendations, and collaboration across Engineering, Product, and Data leadership.

Unpaid AI engineering internship to design, build, and deploy ML solutions (NLP, computer vision, recommenders). Work on data preprocessing, model training/fine-tuning, containerized FastAPI microservices, and scalable deployments on Google Cloud Run. Contribute to code reviews, GitHub repos, and integration of third-party AI APIs.

Build and scale production agentic AI systems for a regulated fintech platform. Responsibilities include architecting AI infrastructure, improving context, memory and state management, developing evaluation systems, integrating AI features with TypeScript and Node.js, enforcing security and privacy controls, and establishing AI-native engineering practices. The role requires reliable, production-scale AI experience and comfort working in a fast-moving startup environment.