Top AI Engineer Jobs in Denver & Boulder, CO

The Principal AI/ML Engineer will develop machine learning models for campaign optimization in AdTech, collaborating across engineering and product teams to enhance advertising capabilities.

Lead the creation of innovative AI solutions for healthcare systems, serve as a trusted advisor, foster collaboration, and drive operational excellence while managing complex challenges in healthcare AI.

Design, build, deploy, and maintain scalable, resilient backend services for a Voice AI platform. Lead cross-cutting projects, make architectural decisions, integrate AI platforms, implement observability and evaluation frameworks, and mentor engineers.

The Principal AI/ML Engineer will develop AI/ML solutions for hardware and firmware validation, enhance test efficiency, and collaborate across teams to implement data-driven analytics and automation.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

The Senior AI Platform Engineer will lead technical onboarding to the Databricks platform, connect data sources, and implement governance and cost management for AI use cases.

The role involves engineering self-healing AI systems with high observability, developing RAG pipelines, and automating incident resolution using Python and Ansible.

As a Senior AI/Data Engineer, you will design and maintain scalable data and AI infrastructures, partnering with cross-functional teams to deliver reliable data systems and support AI-driven business capabilities.

Design, build, and ship production LLM-powered features for Gusto Retirement. Implement and integrate LLMs (RAG, agents, prompting, tool loops), make pragmatic architecture decisions, collaborate with product, design, and engineering, write reliable well-tested code, and drive end-to-end execution and iteration.

Develop AI solutions for marketing teams, integrating AI agents and workflows while coaching teams for self-sufficiency in AI use.

As a Senior AI Engineer at Knotch, you will develop AI agents for workflow automation, design backend and frontend services, and collaborate with teams to integrate AI into products. Your role includes optimizing systems for performance and ensuring agent safety and governance.

Lead architecture and delivery of agentic AI infrastructure: multi-agent orchestration, real-time conversational reasoning, retrieval/memory systems, LLM optimization, safety/observability, and secure tool integration. Mentor senior engineers and collaborate cross-functionally to build production-grade autonomous agents for enterprise workflows.

As a Lead Data Engineer, you'll design and implement AI-powered data systems, focusing on natural language queries and scalable frameworks. Collaborate with teams to optimize data access and enhance reporting tools.

The role involves developing AI-powered features, including agent capabilities, ensuring their reliability and scalability, and collaborating with cross-functional teams. Candidates should have strong hands-on experience with LLMs and Python in production environments.

The Full Stack AI Engineer will develop and maintain AI agents, build clinician-facing products using React and Python frameworks, and manage data infrastructure to support clinical workflows.

Design and build autonomous, enterprise-grounded AI agents and workflows using the Elastic Stack, RAG, and LLMs. Integrate models with internal APIs and SaaS, manage deployment and scaling on cloud/Kubernetes, ensure security/compliance, document systems, and provide technical leadership for agent lifecycle and performance optimization.

The Senior AI Engineer designs and deploys AI agents and workflows, collaborating across teams to deliver reliable AI solutions that drive business outcomes.

The Senior AI Developer will build and maintain production-grade AI solutions, manage data pipelines, and collaborate on technical standards within the AI Operating Group.

The AI Engineer will design backend systems for workflows, optimize multi-agent pipelines, develop knowledge retrieval systems, and collaborate to integrate workflows using LLMs and traditional systems.

Maintain and expand the companys foundational AI platform codebase; support project teams to increase reuse (50-70%); fold successful project work back into the platform; address bugs and performance issues; create training, documentation, and onboarding; provide pairing and code reviews; and collaborate with sales and delivery teams on platform capabilities and project requirements.

Lead client-facing AI engineering engagements: build and deploy production LLM systems using RAG, vector DBs, and agent frameworks; handle cloud deployments, CI/CD, and reliability; debug enterprise issues (auth, networking, data); embed with client teams and document solutions for reuse.

Lead and deliver an enterprise AI platform and agent factory: design architecture, build cost-effective model infrastructure, harden security/SSO/IAM, enable developer adoption, and build agent workflows across the PDLC (ideation, development, deployment). Mentor engineers, steer delivery partners, and remain hands-on implementing agents, RAG pipelines, integrations, and observability to drive measurable productivity gains.

Build and deploy production AI capabilities for mental healthcare: full‑stack development, agentic systems, RAG and retrieval pipelines, evaluation frameworks, safety guardrails, observability, CI/CD, and cross‑functional collaboration to ensure safe, reliable AI in clinical workflows.

Lead design and build scalable autonomous and semi‑autonomous multi‑agent AI systems. Implement RAG pipelines, tool‑calling agents, memory, observability, and AgentOps. Ensure production readiness, safety, governance, and secure data handling. Provide technical leadership, mentor engineers, and shape platform standards across teams.

Design, develop, and optimize prompts for conversational AI and integrate them into NLP/LLM systems. Build RESTful APIs (Python/Django/Flask) and React front-ends, evaluate prompt effectiveness with metrics and user feedback, collaborate with product, UX, and data teams, and document prompt engineering methodologies.