Top Artificial Intelligence Engineer Jobs in Denver & Boulder, CO

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, build, and ship production-grade agentic AI workflows using LangGraph, Temporal, and Pydantic. Implement AI observability with LangFuse, set AI engineering standards (RAG, prompt management, tool-calling), benchmark LLMs, deploy cloud AI (containerization, cost management), and mentor engineers while supporting enterprise SLA, security, and compliance needs.

As a GTM AI Engineer, design and execute AI-driven workflows for Superhuman's go-to-market stack, improving automation in sales processes and team collaboration.

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.

Design and implement AI capabilities, transition prototypes to production, build agent workflows, optimize data retrieval, and automate evaluation processes.

Use advanced analytics, statistical techniques, and machine learning to extract insights from large datasets. Design data solutions and pipelines, build predictive models (R, MATLAB), and apply TensorFlow and Scikit-Learn for deep learning and NLP. Collaborate with teams and clients, deliver quality work, and adhere to firm standards.

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.

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.

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 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.

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

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

The ERP AI Engineer - Manager at PwC leads teams in AI/ML engineering, manages client relationships, and designs AI solutions, all while mentoring team members and addressing complex business challenges.

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.

Design, build, and operate authentication/authorization and high‑availability security services; strengthen data and API protections; detect and mitigate account takeover and abuse; build security platforms and tooling; address ambiguous AI/LLM security risks and promote engineering excellence.

As a Lead Software Engineer, you'll design and develop GenAI platforms, integrate LLMs, mentor engineers, and solve technical challenges for scalable solutions.

Own reliability, monitoring, and incident response for AI infrastructure; build automation and CI/CD tooling; manage Kubernetes/Docker production workloads; partner with infrastructure, security, and compliance; improve observability and documentation; develop internal full‑stack tooling in Go or Python.

Senior SRE on the IT Operations team owning reliability, monitoring, and incident response for AI infrastructure. Build automation, CI/CD and Kubernetes tooling, improve observability and documentation, and develop internal full-stack tools using Go or Python. Partner with Infrastructure, Security, and Compliance to scale secure, resilient AI deployment pipelines.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

Build and productionize agentic workflows and RAG pipelines, integrate LLMs into the GTM stack, and develop high-performance backend services and ML pipelines. Connect quoting engine, Salesforce, and data lakes, solve synchronization and architectural challenges, and ensure observability, scalability, and reliability for sales automation tools.

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.