Top Artificial Intelligence Engineer Jobs in Denver & Boulder, CO

As a Staff AI Engineer, you'll lead AI product development, overseeing technical direction, system deployment, and model evaluation while collaborating across teams.

The role involves designing AI systems for mental healthcare, focusing on building scalable, production-grade systems that enhance patient engagement and clinical operations. Responsibilities include setting technical standards and ensuring safety and reliability while working with advanced AI technologies.

As an AI Engineer, you will build and scale AI business applications, focusing on generative AI. Responsibilities include rapid prototyping, applying software engineering best practices, and providing production support.

The AI Engineer Manager leads the Data and Analytics Engineering team in developing AI solutions, manages client accounts, analyzes data, and fosters team leadership and relationships with clients.

Lead design and implementation of agentic AI workflows and integrations for Acquia DAM. Build, ship, and observe production AI features (auto-tagging, search, governance), set AI engineering standards, evaluate models and tooling, mentor engineers, and represent AI architecture to product and customers.

Take Deepgram's speech models and optimize, compile, and run them on resource-constrained embedded and edge hardware. Work across model compression, runtime performance (C/C++/Rust), RTOS/bare-metal and embedded Linux environments, vendor NPUs/DSPs and inference runtimes, benchmarking, deployment pipelines, OTA updates, and partner with silicon vendors to enable real-time, low-power on-device voice experiences.

As a Senior AI Engineer, you'll design and implement AI-driven solutions, focusing on full-stack development and production-grade applications while mentoring junior team members.

Architect and scale an internal AI platform and APIs to integrate LLMs into developer workflows. Build autonomous agents, RAG systems, and production-grade tooling to boost developer productivity, ensure reliability, security, and cost-efficiency, and collaborate across product, infrastructure, and security teams.

As a Principal AI Engineer, you'll design production-grade systems integrating generative AI in mental healthcare, overseeing architecture and applied research.

The Staff Applied AI Engineer will develop decision intelligence systems using machine learning and optimization, focusing on improving planning decisions and operational efficiency. Responsibilities include building models for revenue attribution, designing decision-support systems, and ensuring security in AI-driven capabilities.

Design, prototype, and deploy LLM-powered agents, RAG workflows, and automations that accelerate internal teams and customer-facing product features. Integrate AI systems with SaaS tools, build reusable components and evaluation frameworks, embed with business teams to deliver measurable outcomes, and establish standards for responsible AI, monitoring, and cost control across the company.

Lead the development of innovative AI solutions, guide teams through complex challenges, and collaborate with diverse stakeholders to enhance operational effectiveness and achieve strategic objectives.

This role involves defining AI architecture, improving system quality, leading teams, and building production-ready AI systems at Drata.

The pega6 Fellowship is a 12-month program for recent CS grads, providing training to build AI software and transitioning them into full-time roles.

Lead and execute assembly, integration, and test (AI&T) campaigns for spacecraft hardware. Define AI&T flows, test strategies, plans, and procedures; verify hardware in cleanrooms and labs; perform RF and end-to-end system validation; identify GSE, facilities and equipment; produce cost/schedule estimates; support proposals; lead and mentor engineering and technician teams; drive test execution, risk reduction, and continuous improvement.

Design, build, and operate Jellyfish's Databricks Lakehouse and data/AI platform. Build ingestion, transformation, and delivery pipelines; govern data with Unity Catalog; enable BI, agentic analytics, metadata, lineage, and observability. Provide technical leadership, set architecture and standards, mentor peers, and collaborate with cross-functional stakeholders to make Databricks the foundation for analytics and AI.

Own and improve the AI Social render pipeline and agent loops that generate on-brand Instagram carousels and reels. Build prompts, structured outputs, tool-calling, and media composition to ship high-quality generative image, video, voice, and music products in collaboration with product, design, and content strategy.

Build and operate Coinbase's LLM and agent platform: unified model gateway, AI Hub, secure microVM agent runtime, knowledge base integrations, AI FinOps, observability and evaluation tooling, and production-grade agent deployments with teams across the company.

Build and maintain AI platform infrastructure, connectors, API integrations, and custom AI-powered applications. Implement authentication, data pipelines, and tooling to embed LLM capabilities across teams. Contribute code, documentation, reviews, and enablement while ensuring security and compliance for government requirements.

Build and lead an enterprise AI platform: design infrastructure, integrations, and custom AI applications. Own provider/tool selection, deploy production model pipelines and connectors, ensure compliance with government security requirements, mentor engineers, and deliver company-wide AI enablement and training.

Build and operate an enterprise AI platform and integrations that connect LLMs to internal systems. Develop production AI applications, manage model/provider evaluations, ensure security/compliance for government environments, and deliver company-wide AI training and enablement.

The AI Platform Engineer II will manage Databricks environments, implement automation and governance for AI infrastructure, and ensure platform reliability.

Lead architecture and delivery of AI-native features for a multi-tenant SaaS platform. Drive cross-functional technical initiatives, design scalable microservice systems, integrate LLMs and agent frameworks, set engineering standards, and mentor engineers to elevate technical execution.

Architect and implement enterprise GenAI platforms (RAG and agentic systems), design knowledge retrieval, orchestration, tool-using agents and human-in-the-loop workflows, ensure production readiness and governance with product/data/security teams, define platform engineering best practices, and solve complex platform challenges with broad autonomy.

Customer-facing engineer who designs, builds, and productionizes GenAI applications (RAG, multi-agent, Text2SQL, fine-tuning). Own production rollouts, advise customers, collaborate with product and engineering, present at conferences, and optimize/evaluate models and deployments across cloud platforms.