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

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.

Design, develop, deploy, and support AI solutions including generative AI applications, agents, workflow automation, document intelligence, and retrieval-augmented generation. Partner with business and IT teams to identify use cases, integrate enterprise platforms and data sources, establish prompt and evaluation standards, support AI governance and security, monitor solution performance, and train employees on responsible AI adoption.

Architects and develops AI-powered systems, tools, agents, backend services, APIs, and experimentation frameworks using foundational AI models. Integrates AI capabilities with microservices, AWS, Snowflake, and complex data systems while ensuring scalability, reliability, and maintainability. Translates business problems into technical solutions, prototypes AI applications, collaborates with product and engineering teams, evaluates emerging AI technologies, and documents system designs and integration patterns.

Build and ship production AI systems across retrieval, knowledge graphs, inference, agentic workflows, and data platforms. Develop customer-context retrieval, scored signals, evaluation and observability capabilities, and workflows that turn insights into action. Work hands-on with large and small models, durable orchestration, cloud deployment, inference-cost optimization, and high-volume observability data. Establish reusable engineering patterns, collaborate with product and platform teams, and mentor through strong technical contributions.

Build production AI agents and agent-powered features from research and prototyping through deployment. Develop evaluation infrastructure with golden datasets, regression suites, and offline/online scoring. Own retrieval and context pipelines, production instrumentation, failure taxonomies, and cost and latency budgets. Partner with data science and product management on experiments and agent behavior, while establishing LLM engineering patterns, guardrails, and reusable tooling.

Lead design, build, and deploy of large-scale data platforms for LLMs, RAG, and agentic AI systems. Hands-on coding, architecting fault-tolerant pipelines, establishing MLOps/DataOps best practices, mentoring engineers, and operationalizing research into production across Exabyte-scale distributed systems.

Owns the strategy, architecture, and implementation of production AI and machine learning systems for personalization, search ranking, relevance, and patient-provider matching. Builds recommendation engines, retrieval models, LLM applications, and scalable ML infrastructure while establishing AI safety, evaluation, and MLOps standards. The role requires hands-on model development, technical leadership, mentoring, and responsible deployment of AI in a regulated mental healthcare environment.

Lead Affirm’s enterprise AI security review program, threat model LLM and agentic systems, review code and configurations, and develop security guardrails, tooling, policies, and monitoring. Evaluate AI-enabled SaaS vendors, identify emerging vulnerabilities, support incident response, and lead cross-functional initiatives across Security, Engineering, Legal, Privacy, and Compliance. The role requires expertise in AI security architecture, threat modeling, IAM, cloud infrastructure, policy-as-code, and regulated enterprise environments.

Lead DigitalOcean’s AI security strategy, governance framework, and multi-year roadmap. Secure AI/ML systems across data ingestion, training, model serving, RAG, agents, and monitoring. Conduct threat modeling, adversarial testing, AI red teaming, and security reviews; develop guardrails and automation with engineering teams. Advise executives and the board on AI risk, define organizational controls, and guide responses to emerging threats including prompt injection, model supply-chain risks, and AI-enabled social engineering.

Lead architecture and implementation of autonomous inbound/outbound AI agents, build scalable LLM ops and signal-intelligence data pipelines, integrate identity resolution and third-party CRM systems, and maintain production-ready, well-documented systems for Series B diligence.

Create reinforcement learning environments to evaluate AI systems solving complex software engineering tasks with MCP tools. Responsibilities include designing reproducible environments, deterministic verification, golden reference solutions, bug fixes, feature implementation, codebase refactoring, and performance optimization across software codebases.

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.

Design, build, test, deploy, monitor, and maintain production LLM-based agentic solutions for Commercial Customer Success. Integrate agents with CRMs and Samsara systems, define evaluation frameworks and success metrics, implement automated tests and monitoring, iterate on models and prompts, and demonstrate impact on customer outcomes and retention.

Designs, develops, and operates enterprise-grade platforms for AI agents and Generative AI applications. Responsibilities include agent orchestration, LLM and AI service integration, secure tool and API invocation, prompt lifecycle management, governance and safety controls, observability, model evaluation, deployment, troubleshooting, and platform optimization. The role collaborates with engineering, security, product, and data teams to deliver scalable, reliable, and compliant AI solutions.

Designs, builds, and operationalizes production-grade generative AI and machine learning systems across AWS and GCP. Responsibilities include LLM applications, agentic workflows, RAG architectures, vector search, model lifecycle management, cloud-native microservices, responsible AI governance, HIPAA compliance, and optimization. The role also leads architecture, collaborates with product, security, data, and compliance teams, mentors engineers, and manages team performance.

Build AI systems for legal teams by designing legal datasets, annotating training and benchmarking data, defining model requirements, translating legal workflows into structured processes, validating product capabilities, engaging clients, and prototyping with AI tools and scripting. Requires a JD and active or prior bar membership, 2–8+ years of legal practice, strong familiarity with litigation, investigations, compliance, or e-discovery workflows, and comfort collaborating with engineers and product teams.

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 production ML and LLM systems, including RAG pipelines, agentic workflows, fine-tuning, and chatbot architectures. Own technical delivery from architecture through monitoring and iteration, collaborate directly with enterprise clients, mentor junior ML engineers, and help establish internal technical standards and tooling.

Build and integrate conversational AI systems for outbound voice calls, chatbots, and call auditing. Design dynamic conversation flows, intent and objection handling, escalation logic, knowledge bases, transcription and speech-analysis pipelines, and QA scoring. Integrate transcripts, summaries, and outcomes with GoHighLevel or other CRMs, while supporting telephony portability, restricted data access, and future client deployment. The contractor owns end-to-end execution and submits a proposed scope, timeline, hours, and cost.

Build two AI-assisted reporting systems that consolidate account and internal data, validate KPIs, automate refreshes and recurring report delivery, generate AI summaries, and produce dashboards. The contractor will independently manage requirements, integrations, error handling, documentation, cost assumptions, timeline, and execution under a fixed-scope project engagement.

Own product-layer performance for agentic AI systems in healthcare. Design prompting, retrieval, context, memory, tool use, evaluation, escalation, and human-review workflows. Build production-grounded evaluations, diagnose failures, improve accuracy, reliability, latency, cost, and steerability, and approve model changes. Partner with Product and Engineering while maintaining model cards, intended-use guidance, limitations, and failure-mode documentation for safe, transparent deployment.

Build and modernize enterprise data and AI ecosystems, including real-time streaming pipelines, data products, semantic layers, knowledge graphs, RAG architectures, and GenAI infrastructure. Develop synthetic data workflows, automated data quality systems, and reliable cloud data platforms. Collaborate with architects, DevOps, and stakeholders, mentor engineers, lead prototypes, and apply AI solutions to insurance use cases while ensuring scalability, governance, monitoring, and operational resilience.

Build and deploy AI-powered solutions across Sales, Marketing, and Customer Support. Diagnose workflows, identify constraints, evaluate whether AI is appropriate, and own initiatives from discovery through deployment. Develop LLM applications using prompt engineering, RAG, agentic architectures, orchestration, guardrails, and enterprise integrations. Measure business value through flow metrics, adoption, ROI, and feedback loops while partnering with technical and non-technical stakeholders.

Design, deploy, and operate production clinical AI agents that synthesize patient data, retrieve clinical knowledge, use tools, generate care recommendations, and escalate to clinicians. Own LLM workflow reliability, observability, cost efficiency, evaluation, safety testing, RAG optimization, orchestration, and lifecycle management. Collaborate with clinical, product, and engineering teams to build reliable AI systems for chronic disease care.

Leads architecture, development, and delivery of secure cloud, data, process intelligence, and AI solutions for federal clients. Builds AWS cloud-native services, scalable data platforms, Celonis process-mining solutions, and agentic AI workflows using Amazon Bedrock and Azure OpenAI. Owns technical direction, engineering standards, security, testing, observability, deployment automation, and reusable product packaging. Supports A-123 continuous controls monitoring, agency implementations, demonstrations, and cloud environment deployments while collaborating with AWS and Celonis partners.