Top AI Engineer Jobs in Denver & Boulder, CO

Design, build, and deploy AI-powered applications, agents, and copilots using LLMs and RAG. Integrate AI into enterprise systems, implement governance and security controls, optimize prompts and models, and deploy scalable solutions primarily on Azure.

Design, build, and deploy Microsoft Copilot and Power Platform solutions (PowerApps, Power Automate, Dataverse) and locally-run AI tooling (Claude). Provide solution architecture, prompt engineering, integrations (Graph API/REST), compliance with Azure/Entra/Purview/DLP, mentoring, testing, and adoption support within regulated environments.

Design, prototype, and deploy production-ready AI products using ML and generative AI. Lead cross-functional teams, collaborate with stakeholders to solve workflow problems, build scalable prototypes and proofs-of-concept, and apply latest ML/GenAI research techniques.

Build and operate AI-powered engineering automation, MCP servers, and agent-ready platform services on Kubernetes. Partner with developers to automate workflows (PR reviews, releases, Slack/GitHub/Linear automations), run agentic workloads securely, and improve observability and secrets handling to accelerate engineering productivity.

Join as a founding full-stack engineer to build an AI-native enterprise platform (agents, connectors, RAG, workflows). Own end-to-end product areas, design reusable primitives, ensure production readiness, work directly with customers, and shape roadmap and engineering practices.

Design, develop, and deploy production-ready AI systems (deep learning, NLP, computer vision). Build APIs and microservices, integrate models into software stacks, optimize inference performance, maintain and retrain models, and research generative AI and advanced ML techniques.

Design, evaluate, and productionize generative AI/ML solutions (RAG, agents, embeddings, retrieval). Build evaluation frameworks for hallucination detection, benchmark LLMs, optimize prompts/models, create datasets, fine-tune models, and collaborate to deploy and monitor enterprise-scale GenAI systems.

Design, build, deploy, and operate production-grade AI agents and multi-agent systems. Implement RAG, agent orchestration, copilots, observability, memory patterns, and enterprise integrations while ensuring governance, reliability, and onshore/offshore delivery.

Join a talent network to build full-stack, AI-native products and platforms. Design and implement LLM workflows, agentic systems, RAG/GraphRAG, vector DBs, frontends (React/Next.js), backends (Python/Node.js/FastAPI/Django), integrations, asynchronous workflows, and production infrastructure while working closely with founders and product teams to convert ambiguous problems into scalable solutions.

Operate and improve large-scale HPC GPU clusters for AI model training and inference. Build observability, automation, CI/CD, and incident response tooling; lead on-call rotations, ensure security/compliance, collaborate with ML engineers, and optimize capacity and costs for production ML workloads.

No responsibilities or details were provided. The posting only lists the job title "AI Engineer" with no additional information about duties or expectations.

Own and evolve ShiftKey's AI infrastructure: production retrieval/indexing, agentic platforms on AWS, graph-based multi-hop retrieval design, platform APIs, reference agents, and cost/observability guardrails for regulated (HIPAA) workflows.

This role involves developing and optimizing LLM applications, architecting workflows, and integrating AI functionalities. Strong experience in AI/ML and collaboration is required.

The role involves building and scaling LLM-powered applications, managing the full product lifecycle, ensuring system reliability, and collaborating across teams to deliver impactful AI-driven features.

Design, train, test, deploy, and maintain deep learning computer vision models focused on sensor fusion and target tracking. Collaborate cross-functionally to implement real-time, embedded and GPU-accelerated systems (Python, C/C++, Rust, CUDA/RAPIDs), contribute to fire control system development, and support architecture and deployment (microservices, Kubernetes) for space and missile-warning solutions. Requires security clearance.

Lead end-to-end development of AI-powered features and shared AI services, own product loop from discovery to production, design data and system architecture, create reusable tooling and guardrails, mentor engineers, and ensure secure handling of sensitive school and family data.

Design and deliver AI-enabled features for a web-based government workflow application using LLMs, RAG, and agentic patterns. Build POCs and production-ready systems, implement orchestration frameworks and prompt engineering, integrate AI/LLM APIs, maintain full-stack apps, establish observability and guardrails, and ensure security and CI/CD practices.

Design and deploy end-to-end Agentic and Generative AI solutions: architect multi-agent workflows, implement RAG pipelines, build prompts and retrieval, create data pipelines and MLOps deployments, engage clients, run workshops, and ensure AI governance and reproducibility.

Design, build, and govern a centralized knowledge foundation for enterprise AI. Audit, structure, and optimize content for retrieval and AI use; define taxonomy and content standards; capture institutional knowledge; partner with stakeholders to keep knowledge current and reusable; improve retrieval quality and governance for downstream AI applications.

Early-career role to build, operate, and improve cloud data/analytics platforms (Fabric, Snowflake, Databricks). Support platform operations, CI/CD for analytics assets, onboarding for BI tools (Power BI/Tableau), runbooks, monitoring, incident response, and basic security/governance. Work under senior mentorship and contribute to automation, deployment templates, and performance triage.

Design, build, and deploy AI-powered internal tools and intelligent workflows to automate People, Finance, Recruiting, and operations. Identify improvement opportunities, translate business needs into technical solutions, own end-to-end execution, and collaborate across teams to ship reliable production software and AI integrations.

Design, build, and ship AI-powered internal tools and intelligent workflows to automate operations across People, Finance, Recruiting, and other teams. Own end-to-end execution from discovery to production, integrate AI services, work cross-functionally, and iterate on deployed systems.

Design and build AI-powered internal tools and automations to improve operations across People, Finance, Recruiting, and business workflows. Own end-to-end delivery from discovery to production, integrating APIs, databases, cloud infrastructure, and AI systems while collaborating with cross-functional teams.

Lead implementation of AI strategy and integrate generative AI/LLM capabilities into client applications. Build and operate cloud-native and on-prem model infrastructure, deliver POCs/MVPs with cross-functional teams, optimize LLM cost/token efficiency, and ensure responsible AI, security, and compliance across commercial and public sector environments.

The role involves architecting AI solutions, optimizing ML systems, and deploying advanced retrieval frameworks. Engineers will lead ML lifecycle from research to production, collaborating across teams to build AI-first products.