Top Artificial Intelligence Engineer Jobs in Denver & Boulder, CO

Lead teams of data scientists and ML engineers to design and deploy AI solutions using Oracle tools and modern AI/ML practices. Manage clients, translate business problems into AI architectures, drive innovation, ensure quality, and mentor staff.

Lead and mentor AI/ML teams to design and deliver AI solutions using Oracle data tools and cloud-native architectures. Manage projects and client relationships, translate business challenges into AI strategies, implement MLOps and scalable deployments, ensure quality and integrity, and contribute to business development.

Design, implement, and deploy advanced AI/ML solutions using Oracle and modern ML tooling. Analyze complex problems, mentor junior staff, build client relationships, manage data pipelines and model lifecycle (CI/CD and monitoring), and contribute to cross-functional delivery while upholding professional and ethical standards.

Lead and manage AI and data engineering teams to develop ML and analytics solutions. Oversee project planning, budgeting, execution, and quality assurance; manage client accounts, mentor staff, resolve conflicts, and drive continuous improvement and production-grade monitoring.

Join PwC's DCM AI Labs to design and implement AI/ML solutions, analyze complex problems, mentor junior staff, build client relationships, and integrate AI into business applications using Oracle and cloud-native technologies.

Lead development and delivery of AI/ML solutions as a Senior Manager: advise on large-scale projects, coach teams, align stakeholders, analyze complex data, and drive production-grade, operational AI innovations.

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.

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.

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.

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.

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.

Build and deploy production micro-apps and LLM-powered agents on a governed GCP platform. Integrate AI with systems (Salesforce, Slack, NetSuite, SQL), harden apps with auth, secrets, CI/CD, logging and monitoring, automate maintenance, and implement evaluation and observability for model quality and drift.

Contribute to building and hardening micro-apps and AI integrations on a shared GCP platform. Complete bug fixes and small features, participate in code and design reviews, apply security and software development best practices, monitor applications and follow runbooks, and use AI tools to assist development, testing, and documentation while learning from senior 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.

Lead development and delivery of healthcare AI/GenAI solutions for health systems: architect RAG and agentic AI workflows, manage global data science teams and MLOps, process clinical and claims data, engage executives, and ensure HIPAA-compliant production deployments with measurable clinical and operational impact.

Lead development and delivery of production-grade AI/GenAI solutions for health systems, architect RAG and agentic workflows, manage global healthcare data science teams, ensure HIPAA-compliant MLOps and model governance, engage executive health-system stakeholders, and translate clinical workflows into measurable AI use cases across clinical, financial, and operational domains.

Consult with clients to design, build, and integrate AI-enabled business applications across enterprise systems. Analyze needs, implement solutions, provide training/support, use data analytics to inform recommendations, and manage project delivery and documentation. Build client relationships and support team leadership in technology-driven business transformation.

Lead development and delivery of production AI/GenAI solutions for health systems. Serve as strategic advisor, guide teams through ambiguity, ensure operational excellence, stakeholder alignment, and adherence to regulated, PHI-handling standards.

Lead development of AI and GenAI solutions for health systems, advise stakeholders, guide teams through ambiguity, ensure operational excellence and compliance, and implement MLOps, model governance, and production AI use cases against clinical data sources.

Design, build, and operate agentic AI systems end-to-end for Professional Services. Translate stakeholder needs into secure, scalable multi-agent architectures, define performance benchmarks, manage deployments and incident response, advise leadership, mentor teammates, and maintain runbooks and production tooling including on-call responsibilities.

As a Staff Applied AI Scientist, you will design and deploy AI systems using LLMs, improve agent performance, and collaborate with engineering to enhance systems for customer impact.