Top AI Engineer Jobs in Denver & Boulder, CO

Design and build production AI applications for enterprise clients, including data processing, retrieval, APIs, tools, integrations, evaluations, and deployment. Partner with client experts to improve system performance and reliability, support cloud, CI/CD, security, and operational processes, and communicate technical tradeoffs clearly. The role also involves automating internal workflows and helping clients adopt new AI-enabled ways of working.

Build and deploy production AI/ML and GenAI systems across the full model lifecycle. Develop RAG pipelines, LLM integrations, data pipelines, labeling workflows, evaluation frameworks, and MLOps infrastructure. Apply AI to manufacturing, computer vision, predictive maintenance, supply chain, forecasting, inventory, supplier risk, and logistics. Collaborate with engineering, manufacturing, and operations teams to deliver measurable business outcomes.

Design, build, deploy, monitor, and optimize production machine learning and LLM systems, including RAG pipelines, agentic workflows, fine-tuned models, and embeddings. The role owns technical delivery from architecture through iteration, collaborates with enterprise clients, mentors junior ML engineers, conducts code reviews, and helps establish internal engineering standards and tooling.

As an AI Demo Engineer, you will create demo applications to showcase AI capabilities, collaborate with sales, and enhance demo content.

Develop and integrate production AI/ML capabilities into Oracle E-Business Suite on OCI for federal financial management. Build NLP and document-processing solutions, REST API integrations, ML lifecycle workflows, cognitive performance metrics, and data-mining capabilities. Participate in SAFe Agile delivery, author user stories, support DevSecOps integration, resolve technical impediments, and contribute to transition and knowledge-transfer materials. The role requires collaboration with architecture, product, and government teams in a cleared defense environment.

Founding Engineer responsible for building FilmoreAI’s production AI and data platform for construction equipment dealers. The role covers agent infrastructure, model routing, LLM extraction and reasoning, canonical data modeling, entity resolution, Python data pipelines, Azure cloud orchestration, evaluation, observability, and safe human-approved write-back. The engineer will work independently in an ambiguous startup environment and use agentic development tools to ship reliable systems with strong cost, latency, accuracy, and operational guardrails.

Build and deploy production-grade agentic AI workflows for Fan Ops and Marketplace Ops, including issue triage, response drafting, dispute resolution, risk scoring, and SLA monitoring. Integrate internal and external data systems, create usable interfaces and retrieval tools, coach nontechnical operators, and own deployment, monitoring, iteration, and impact measurement. The role also evaluates emerging LLM tools and establishes AI-embedded engineering practices across Operations.

Owns the strategy, architecture, and implementation of production AI and machine learning systems for personalization, search ranking, relevance, and patient-provider matching. Builds recommendation engines, retrieval models, LLM applications, and scalable ML infrastructure while establishing AI safety, evaluation, and MLOps standards. The role requires hands-on model development, technical leadership, mentoring, and responsible deployment of AI in a regulated mental healthcare environment.

Design, build, and productionize LLM-powered systems (adaptive content, tutors, evaluators) and AI pipelines (RAG, embeddings, fine-tuning). Prototype and optimize models across modalities, define AI safety/evaluation/observability practices, and collaborate with product and engineering to measure learner outcomes and iterate.

Unpaid AI engineering internship to design, build, and deploy ML solutions (NLP, computer vision, recommenders). Work on data preprocessing, model training/fine-tuning, containerized FastAPI microservices, and scalable deployments on Google Cloud Run. Contribute to code reviews, GitHub repos, and integration of third-party AI APIs.

Develop, research, optimize, and deploy computer vision AI/ML algorithms for remote sensing, including automatic target recognition and multimodal EO/SAR data fusion. Responsibilities include processing large datasets, training supervised and unsupervised deep learning models, deploying solutions to cloud environments, reviewing research, documenting capabilities, and potentially leading junior engineers. The role requires an active TS/SCI clearance and supports defense and commercial applications.

Build and support AI agents, automated workflows, system integrations, and generative AI solutions using Azure AI, Copilot Studio, Power Automate, Tines, and supporting code. Collaborate with stakeholders to define requirements, test and deploy reliable automations, implement logging and security, troubleshoot production issues, document solutions, and measure business outcomes. Integrate platforms such as Teams and Salesforce through APIs, webhooks, and connectors while validating AI-generated outputs.

Build and deploy production AI/ML features across the model lifecycle, including GenAI systems, RAG pipelines, data pipelines, evaluation infrastructure, and MLOps. Apply machine learning to manufacturing, supply chain, computer vision, predictive maintenance, forecasting, inventory, and logistics problems. Collaborate with engineering, manufacturing, and operations teams to deliver measurable outcomes.

Operate and improve large-scale HPC GPU clusters for AI model training and inference. Build observability, automation, CI/CD, and incident response tooling; lead on-call rotations, ensure security/compliance, collaborate with ML engineers, and optimize capacity and costs for production ML workloads.

Design, build, deploy, and operate secure, scalable AI-enabled products and internal tools. Evaluate models and services, develop prompts and context strategies, establish evaluations, monitoring, safeguards, and reusable architectures, and guide responsible AI adoption. Partner with product and engineering teams to prioritize opportunities, balance value and risk, mentor colleagues, and improve AI systems using production data and stakeholder feedback.

Design, build, deploy, monitor, and optimize production ML and LLM systems, including RAG pipelines, agentic workflows, fine-tuning, and embeddings. Own technical delivery from architecture through iteration, collaborate with client engineering and product teams, mentor junior ML engineers, and help establish internal technical standards and tooling.

Develop and deploy AI-enabled applications, web interfaces, and workflow integrations using low-code platforms and modern programming frameworks. Build Python and SQL automation, integrate generative AI and NLP capabilities, collaborate with data and business teams, and support testing, troubleshooting, Agile delivery, deployment, and incident management. Ensure solutions meet usability, accessibility, compliance, and performance standards.

Design, develop, optimize, and deploy scalable computer vision and vision-language systems using classical computer vision and deep learning. Build evaluation frameworks, monitor production models, debug issues, and improve architectures with new data. Collaborate with product managers and cross-functional teams to deliver practical AI solutions across cloud and edge environments.

Design, build, and operate AI-powered automation, CI/CD systems, distributed services, developer tooling, and code-intelligence platforms. Own significant capabilities from architecture through production, improve engineering workflows using measurable evaluations, scale reliable automation and delivery systems, and operate secure multitenant services. Lead technical reviews, define reliability practices, integrate platforms through APIs and MCP, mentor engineers, and drive ambiguous technical problems to dependable production outcomes.

Build and maintain core AI framework infrastructure, including Python APIs, graph execution, training workflows, validation systems, numerical parity tests, and reusable framework components. Develop JAX and PyTorch tooling, improve research-to-production workflows, and collaborate with researchers and hardware engineers. The role also involves API design, testing, documentation, developer ergonomics, and shaping software architecture connecting model design, simulation, training, validation, and hardware execution.

Leads architecture and hands-on development for AI frameworks, performance systems, benchmarking, and developer tools. Investigates issues across models, compilers, runtimes, services, and accelerator hardware; improves model onboarding, runtime performance, reliability, hardware utilization, and Azure efficiency. Establishes scalable measurement and observability capabilities, influences cross-team technical strategy, delivers production solutions, and mentors engineers while advancing engineering quality and maintainability.

Architect and build production agentic AI systems for customer support and compliance automation. Lead technical direction for distributed systems, APIs, orchestration frameworks, guardrails, and evaluation systems. Create reusable automation primitives, improve grounding and reliability, reduce hallucinations, and mentor engineers on LLM application design and production readiness. Collaborate cross-functionally to scale AI automation across Coinbase.

Lead the design, development, deployment, and monitoring of production AI/ML applications for dairy-farm intelligence. Responsibilities include data wrangling, feature engineering, model selection, experimentation, MLOps, agent evaluation, observability, fine-tuning, AI governance, and integration with cloud infrastructure. The role also drives AI adoption, establishes engineering practices, collaborates cross-functionally, and may build React and TypeScript user experiences.

Build Placer.ai’s internal AI platform, including autonomous agents, MCP servers, SaaS and internal-tool integrations, authentication infrastructure, Claude plugins, Databricks data pipelines, usage analytics, and scalable platform infrastructure. Establish connector standards, optimize costs, triage integration requests, and implement security and compliance controls while partnering across engineering, data, operations, and business teams.

Own Gusto’s agentic coding platform, including autonomous coding workflows, AI code review, orchestration, security posture, sandboxing, permissions, infrastructure contracts, and AI spending controls. Lead and grow a senior engineering team, recruit and calibrate talent, set the product and technical roadmap, manage ambiguity across multiple domains, and advise executive leadership on AI-native software development strategy.