Top AI Jobs in Denver & Boulder, CO

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.

Scale and secure the enterprise Databricks platform on AWS for production analytics, ML, and AI agent workloads. Administer workspaces, Unity Catalog governance, CI/CD pipelines, Terraform automation, MCP server integration for agent frameworks, cluster policy enforcement, cost attribution, monitoring, incident response, and mentor junior engineers.

Lead and scale GM's enterprise Privacy and AI governance operations: build workflows, training, metrics, reporting, and documentation; operationalize AI impact assessments and governance platform; drive continuous improvement, automation, and cross-functional stakeholder alignment to strengthen privacy and responsible AI program maturity.

Design and build AI-powered prototypes and end-to-end workflows using LLMs to solve creative and operational problems. Partner with Services leaders to prioritize opportunities, translate ideas into roadmaps, enable adoption through documentation and training, and collaborate with engineering to productionize solutions.

Perform data science on classified datasets: build and evaluate ML/AI models (including LLMs/agentic AI), clean and retrieve data, visualize findings for non-technical leaders, and support DoW/IC clients while holding required security clearance.

Lead design and delivery of scalable, cloud-native AI systems and agentic automation. Own system-level architecture, drive DevOps/CI/CD and operational excellence, mentor engineers, resolve architectural bottlenecks, and align technical solutions with business objectives.

Lead data science engagements with customers to acquire, clean, analyze, and model data; develop scalable operational models and evaluation methods; coach engineers on best practices; drive solution strategy, ethics, and industry thought leadership; write extensible code and collaborate across teams to deliver cloud-based AI solutions.

Lead and mentor a data science team to design, build, and deploy advanced analytics and AI/ML solutions (including LLMs/agentic AI) for classified DoD/IC clients. Translate complex data into visual and written narratives, advise leadership on risks and go/no-go criteria, and identify new data-driven opportunities. Work includes data retrieval/cleaning, modeling, evaluation, and visualization.

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.

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.

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.

The role involves leading technology implementation and operations, advising CIOs on AI strategy, and producing actionable insights based on market trends and data for various industries.

Manage and coach a team of mentors to ensure delivery outcomes for ~200 AI fellows at scale. Build delivery systems, run operational cadence, lead live instruction when needed, translate field signals into curriculum changes, and partner with strategic partners to close the feedback loop between mentors, fellows, and the curriculum team.

Lead end-to-end development and deployment of predictive and hybrid deep learning/LLM models for insurance use cases. Prototype, validate, and productionize models using modern ML frameworks and MLOps, collaborate with product and engineering, and communicate technical results to stakeholders to drive measurable business impact.

Lead high-impact modeling initiatives using deep learning, LLMs, and traditional methods. Prototype, deploy, and monitor hybrid models on large insurance datasets, drive MLOps standards, and communicate results to nontechnical stakeholders to realize business impact.

Lead complex modeling projects combining deep learning, LLMs, and traditional techniques. Prototype, deploy, and monitor production models, mentor data scientists, collaborate with engineering/product, and translate results into business impact for insurance customers.

Lead deployment of AI and automation across Global Media Operations: identify opportunity areas, define AI strategy and reference architecture, manage teams and vendors, deliver end-to-end solutions, develop SOPs and training, measure adoption, and partner with Product, Engineering, and Enterprise AI to ensure secure, scalable implementations.

Lead and scale 3Cloud's Advisory service line and enterprise AI agenda. Set strategy, operating model, offerings, governance, and cross-functional alignment. Drive commercial performance, develop leadership, embed AI into advisory portfolios, and ensure measurable business outcomes. Sponsor major pursuits and establish repeatable go-to-market motions and operating discipline.

Lead and manage the engineering team to design, build, deploy, and maintain cloud-based social media/influencer marketing and AI platforms. Oversee software architecture, large-scale data pipelines, image/video processing, and AI/ML solutions. Coordinate cross-functional work, hiring, project planning, Agile delivery, and continuous system improvement.

Own and administer Salesforce and NetSuite while designing and deploying production-grade AI-driven automations and agent workflows. Ensure data governance, PHI/HIPAA compliance, release management, and quote-to-cash integrity. Partner cross-functionally to prioritize work, coach teams, document systems, and measure impact of automation on operations and revenue.

Lead hardware strategy, performance architecture, and benchmarking for large-scale Microsoft 365 and AI workloads. Investigate performance, scalability, and efficiency across compute, GPU, memory, storage, networking, and accelerators. Partner with Azure and platform teams, build AI-assisted performance engineering tools, and influence cross-organizational architecture and infrastructure decisions.

Lead complex data exploration and AI/ML projects for Intelligence Community missions: develop and operationalize models, apply statistics and data mining, produce analytical products, guide teams, and publish research while maintaining technical documentation and security requirements.

Lead advanced data science and analytics projects for the Intelligence Community, applying AI/ML, predictive modeling, model operationalization, containerization, and cloud platforms. Translate complex technical findings into actionable visual and written products, publish research, and maintain technical documentation while working under TS/SCI clearance requirements.

Design, build, and operate large-scale LLM infrastructure and data platforms for training, fine-tuning, and inference. Provision GPU clusters, optimize GPU utilization, implement model lifecycle management, deploy inference frameworks, create evaluation and observability systems, and collaborate with data scientists to productionize AI capabilities. Mentor engineers and enforce MLOps/DataOps best practices.

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.