Top Artificial Intelligence Engineer Jobs in Denver & Boulder, CO

Design and implement scalable AI systems and data pipelines using Python and TensorFlow. Perform data wrangling, ML model development (including NLP and deep learning), integrate diverse data sources, analyze patterns, collaborate with stakeholders, and mentor junior team members to align AI initiatives with business goals.

Design and implement AI systems and scalable ML models (Python, TensorFlow); build/maintain data pipelines and integrations; apply NLP (NLTK) and deep learning for analytics; deploy and optimize models (including open-weight LLMs); collaborate with stakeholders and continuously learn to improve AI solutions and deployment reliability.

Lead and coach teams to design, develop, and deliver production-grade AI and data solutions for clients. Manage client accounts, ensure quality and timelines, analyze complex datasets, implement best practices, and drive continuous improvement while fostering client relationships and technical excellence.

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.

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.

Hands-on technical leader who designs, builds, and deploys production-grade AI automation and agentic workflows. Responsibilities include rapid prototyping, RAG and document-intelligence systems, API integrations, orchestration, monitoring, reusable AI assets, and mentoring a small engineering team to deliver frequent releases and measurable business impact.

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.

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.

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.

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.

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

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.

Design, implement, and maintain core infrastructure and tooling to enable agentic AI development. Drive AI enablement, developer relations, training, and shared platform tooling to support engineering teams and production LLM/agent integrations.

Lead AI security strategy and guardrails, design and review AI gateways and tooling, scale InfoSec review processes, reduce AI-driven security noise, drive remediation and investigations, and mentor security and platform engineers.

Design, build, and own scalable data pipelines and evaluation systems that power production AI features and internal analytics. Ensure data quality across ingestion, modeling, and reporting, collaborate with ML and analytics teams, deploy and monitor ML systems, and establish standards for data work and evaluation.

The Senior Software Engineer for Support AI will develop voice and chat systems for customer support, driving projects from concept to production, while collaborating across teams and improving AI capabilities through integration and data insights.

Design, build, deploy, and maintain AI agents, automations, and workflow tools in partnership with business strategists. Troubleshoot and support Unanet's Glean platform, iterate with end users, measure impact, document reusable solutions, and collaborate with IT and cross-functional teams to scale AI solutions.

Design and build scalable, automated evaluation pipelines and infrastructure to validate speech, audio, and multimodal models. Translate research benchmarks into enforceable pass/fail gates, run batch and streaming tests, operate canaries and continuous monitoring, integrate quality gates into CI/CD, and partner with research, training, inference, and infra teams to prevent regressions and ensure production model quality.

Build, deploy, and maintain AI-powered agents, APIs, and applications for People operations on Snowflake. Turn messy business requirements into production systems, integrate with Workday/Notion/case tools, ensure data governance and security, design multi-model LLM reliability controls, and operate full lifecycle including CI/CD, monitoring, and incident fixes while partnering with non-technical stakeholders.

Lead technical vision for AI and platform infrastructure across the company. Solve ambiguous, high-stakes problems end-to-end, set architecture and paved-road standards, guide AI provider selection, ensure compliance for government environments, mentor engineers, and drive adoption of best practices in DevOps, CI/CD, developer experience, and compute infrastructure.

Lead development and delivery of healthcare AI/GenAI solutions for health systems—architect RAG and agentic workflows, manage global MLOps and model governance, engage executive stakeholders, and drive production-grade use cases across clinical, financial, and operational domains.

Lead development and delivery of AI and GenAI solutions for health systems. Serve as strategic advisor, manage teams, ensure HIPAA-compliant deployments, implement MLOps and model governance, translate clinical workflows into AI use cases, and drive stakeholder alignment and operational excellence.

Build and ship AI-powered features for GTM analytics: Genie agents, LLM-driven workflows, and in-app intelligence. Develop agentic workflows (prompts, retrieval, tool integrations), write Python/SQL against Databricks lakehouse and Lakebase (Postgres), evaluate AI outputs, and collaborate with engineers and stakeholders to deliver tested, production-ready code.

Lead end-to-end front-end development for an enterprise Agentic AI platform. Design interactive, explainable UIs that render streaming LLM outputs, code citations, dependency graphs and authoring workflows. Build real-time experiences with WebSockets/streaming APIs, ensure traceability to source code, collaborate with AI engineers and designers, and drive accessibility, performance, and observability across a FedRAMP-aligned federal deployment.