Top AI Engineer Jobs in Denver & Boulder, CO

Design, build, and deploy production-grade Generative AI applications: multi-agent systems, advanced RAG pipelines, LLM integration and swapping, model fine-tuning and quantization, scalable FastAPI backends, containerized deployment, and monitoring/optimization for latency, cost, and reliability.

Build and deploy generative AI solutions: develop production-grade Python code, LLM-based systems, agent frameworks, RAG pipelines with vector databases, APIs, cloud deployments, and MLOps practices while ensuring model evaluation, guardrails, and observability.

Design, build, and deploy agentic AI and multi-agent systems using LangChain/LangGraph and ADKs. Implement LLM orchestration, RAG pipelines, vector DBs, React-based AI interfaces, and production-grade infrastructure with observability, security, and optimization.

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.

The Senior AI Engineer will design and maintain customer-facing AI tools by building APIs, optimizing workflows, and conducting experiments with generative AI models, ensuring system reliability and integration with production systems.

Design, build, and ship AI-powered observability features using LLMs and agentic workflows. Rapidly prototype, iterate with real users, integrate AI into alerting, runbooks, and developer workflows, and ensure solutions are scalable, maintainable, and production-ready while collaborating cross-functionally.

Lead design, deployment, and monitoring of LLM-based AI solutions. Build end-to-end generative AI applications, fine-tune models, integrate LLM APIs into products, ensure post-deployment performance, and collaborate cross-functionally within an Agile environment to drive measurable business impact in healthcare.

Lead design and implementation of production agentic AI systems that integrate LLMs with Teak's platform APIs. Build RAG pipelines, MCP/tool integrations, evaluation frameworks, and AI infrastructure on cloud (AWS/GCP/Azure). Contribute backend services in Python, ensure compliance and safety, and operate systems in production including observability and on-call duties.

As a Staff Backend AI Engineer, you will design and operate backend AI systems for Arbiter's care orchestration platform, ensuring reliability, low latency, and scalability while collaborating across teams and mentoring junior engineers.

Lead architecture and technical strategy for an off-platform AI tier and agent stack. Design and build MCP server, internal PM and Execution agents, and an External Onboarding Agent. Implement retrieval, evals, observability, and guardrails. Drive design-partner engagements, set engineering standards, and mentor engineers to productionize LLM/agent systems.

The Senior AI Engineer will develop LLM-powered AI systems, build RAG pipelines, and solve complex information retrieval problems while collaborating cross-functionally to enhance Affinity's CRM platform.

The Staff AI Engineer will drive architecture and implementation of AI-assisted engineering practices, overseeing technical standards, automated testing, and code review systems for software development in a regulated fintech environment.

The AI Engineer will design, develop, and deploy AI-powered systems, improve operations with intelligent workflows, and collaborate on high-leverage AI opportunities.

The AI Engineer will develop scalable AI systems, automate workflows, and collaborate with business teams to enhance operational efficiency across OpenSesame.

The Senior AI Engineer will develop AI tools, enhance coding workflows, implement automation, and provide training while leveraging AI coding tools and metrics tracking.

The AI Engineer will assess and optimize AI tool usage in engineering workflows, drive adoption, and present findings to leadership.

The Full-Stack Applied AI Engineer will design and build AI-enabled products, own delivery from UI to backend, and implement robust systems with a focus on security and reliability, collaborating closely with clients and team members.

Lead a team of technical experts in deploying AI-driven customer engagement solutions, ensuring quality, performance, and project goals are met.

Design, build, and deploy enterprise-scale agentic and generative AI systems for healthcare. Architect multi-agent frameworks, implement advanced RAG and multimodal data pipelines, optimize LLM performance and latency, establish MLOps/CI-CD and secure containerized deployments, and collaborate with stakeholders to ensure clinical safety and compliance (HIPAA/HITRUST). Mentor developers and produce production-grade documentation.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

Design, build, and operate production-grade AI systems using ML, LLMs, RAG, and agentic workflows. Define evaluation strategies, implement data/retrieval/orchestration layers, apply MLOps/LLMOps, and partner with stakeholders to prioritize, monitor, troubleshoot, and improve AI solutions balancing quality, latency, cost, reliability, and maintainability.

The AI Engineer develops advanced AI systems, manages datasets, creates and integrates machine learning models, and collaborates with teams to enhance applications.

Develop, train, test, and integrate machine learning models; process large datasets; optimize AI systems; collaborate with cross-functional teams; document architectures; ensure ethical, performant AI solutions using cloud services.

The Principal AI Product Engineer leads the development of AI-integrated software solutions, overseeing architecture and mentoring teams. Key responsibilities include technical leadership, AI integration, task decomposition, and enhancing build pipelines.

The Senior AI Engineer will develop and deploy multi-agent systems, automate workflows, integrate enterprise systems, and mentor junior engineers while ensuring quality and security compliance.