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.

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.

Serves as the senior technical authority for enterprise browser data protection and security. Owns architecture, roadmap, standards, lifecycle, browser-based DLP controls, integrations, automation, and operational support. Leads complex troubleshooting, major incident response, root-cause analysis, runbook development, and continuous improvement. Partners across cybersecurity, identity, endpoint, network, cloud, risk, compliance, and vendor teams while mentoring engineers and influencing enterprise security design.

Leads enterprise AI tooling and security strategy, building secure cloud-native platforms and protecting AI models, agents, prompts, data, APIs, and outputs. Designs controls for identity, access, encryption, monitoring, threat detection, adversarial testing, governance, and incident response. Provides distinguished technical leadership, mentors engineers, establishes engineering standards, and partners across architecture, security, governance, risk, compliance, and business teams.

Leads frontier research and engineering for AI-powered cybersecurity systems. Defines research direction, designs agentic and autonomous security applications, builds evaluation environments, improves systems through feedback and adversarial simulation, and transitions prototypes into scalable production capabilities. Provides technical leadership across teams, mentors senior researchers and engineers, and contributes to research publications, open source, and the broader AI and cybersecurity communities.

Lead the technical direction, architecture, and development of Compass, an LLM-powered agent platform. Design multi-agent systems, MCP and A2A integrations, orchestration, evaluation, and observability infrastructure. Drive cross-team technical projects, production support, engineering quality, mentoring, and adoption of AI-assisted development practices. Partner with Product, QA, Design, Operations, and Engineering stakeholders to deliver reliable agentic systems.

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.

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.

Serve as the senior technical authority for AI-powered drug development applications, especially protocol generation and clinical trial execution. Architect and build production-grade agentic systems, define LLMOps, evaluation, observability, security, and GxP standards, and guide validated software development. Lead technical delivery, mentor engineers, assess models and tooling, and collaborate with clinicians, statisticians, medical writers, clinical operations, and product teams.

Develop generative AI solutions using Python, AWS, LLMs, chatbots, prompt and context engineering, RAG, vector databases, and agentic AI frameworks. Build coding prompts and contexts with GitHub Copilot, Claude Code, and Amazon Q Developer to convert business stories into production code and pull requests. Design autonomous and multi-agent workflows, memory and token-management systems, caching strategies, MCPs, and real-time AI decisioning features. Auto-lending experience and A/B testing expertise are advantageous.

Lead the end-to-end development of production AI capabilities, including LLM-powered agents, personalized guidance, tool integrations, and structured outputs. Prototype, evaluate, launch, monitor, and improve features while maintaining quality, safety, reliability, speed, and cost efficiency. Build and operate Python services, collaborate with product and engineering teams, and contribute effectively in a fast-moving startup environment.

Own the design, adaptation, integration, release, and production rollout of tailored AI solutions for a large client. Collaborate with distributed engineers, contribute client-specific changes to a shared codebase, ensure reliability in a regulated environment, serve as the client’s technical contact, and resolve complex production issues independently.

Leads the design, development, evaluation, and production operation of healthcare AI products, including agentic workflows, RAG systems, model optimization, guardrails, and human-review controls. Partners with product, clinical, engineering, data, platform, and security teams to build safe, reliable, observable, and compliant AI systems. Provides technical leadership through architecture, code reviews, incident response, mentoring, and delivery of HITRUST-aligned solutions for regulated healthcare environments.

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, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

Lead the AI Quality engineering team while remaining hands-on building evaluation infrastructure, observability tooling, diagnostic systems, datasets, annotation workflows, and CI/CD pipelines for production AI agents. Run experiments across prompts, models, routing, caching, and agent harnesses to improve quality, latency, and cost. Partner with AI Core engineering and manage technical direction and team priorities.

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.

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.

Build and own production LLM-powered healthcare applications, including clinical trial matching, ambient copilots, billing automation, documentation, inference pipelines, and real-time speech recognition. Collaborate with product, design, clinical, and go-to-market teams to rapidly launch features, gather user feedback, and improve workflows used by clinicians. The role requires strong AI engineering, product judgment, and full-stack execution skills.

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.

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.

Lead the evolution of trading infrastructure by managing trade operations, P&L attribution, risk monitoring, data integrity, and performance reporting. Develop Python- and machine-learning-based AI agents, MLOps workflows, anomaly detection, predictive risk models, automated reconciliations, and real-time dashboards to transform manual trading operations and improve risk analysis.

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.