Top Remote Artificial Intelligence Engineer Jobs in Denver & Boulder, CO

Design, develop, and deploy AI/ML models and pipelines. Train, fine-tune, and monitor models using TensorFlow/PyTorch/Scikit-learn. Build APIs/microservices, integrate models into production, collaborate with cross-functional teams, and ensure scalability and reliability.

Design, build, and ship agentic LLM systems and orchestration pipelines. Translate stakeholder ideas into production workflows, write and refine prompts, run evals and observability in Weave or similar, and maintain system performance and guardrails.

Design, build, and scale production-grade AI applications using LLMs and RAG architectures. Develop backend services and integrations, deploy and optimize solutions on AWS/Azure/GCP, and implement containerization with Docker and Kubernetes. Collaborate across teams, contribute to architecture, code reviews, documentation, and ensure production reliability and scalability.

Lead design, build, deployment, and maintenance of production-grade GenAI systems (LLM apps, agentic workflows, RAG, retrieval, and evaluation). Architect scalable ML services, implement model serving and observability, optimize latency and cost, fine-tune LLMs, translate prototypes to production, mentor engineers, and produce runbooks and documentation.

Design, implement, and optimize explainable clinical reasoning AI systems that integrate patient data and medical knowledge. Build RAG/embedding pipelines, backend integrations, scalable Docker/cloud-native deployments, and ensure transparent, evidence-backed model outputs while collaborating with cross-functional teams.

Lead architecture, design, development, deployment, and optimization of an agentic AI platform. Partner with product, data, security, and business teams to productionize AI solutions, establish engineering best practices, mentor teams, and drive reusable AI services, automation, and operational improvements to deliver business impact.

Build and productionize GenAI features: design experiments, evaluation suites, advanced RAG and reasoning pipelines, oversee SFT/LoRA tuning, and optimize quality-cost-latency trade-offs for reliable, user-focused AI products.

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

Design and deliver agentic systems for enterprises, integrating tools and data while ensuring safety and compliance. Collaborate with clients to optimize business workflows and system architectures.

Design, build, and evaluate AI systems, focusing on natural language understanding and automation. Collaborate with teams to ensure production quality and performance metrics.

Design, train, optimize, and deploy LLMs for offline and on-prem environments. Build end-to-end LLM pipelines: data preprocessing, SFT, LoRA/Q-LoRA, quantization, RAG, vector stores, MCP integrations, and inference systems. Implement document parsing, GPU optimization, CI/CD for local ML workflows, and maintain model registries and versioning in restricted networks.

Lead end-to-end development of AI-native financial products for credit union members. Use and improve AI agents, build features across a TypeScript stack, integrate with third-party APIs, monitor production, and partner with design and stakeholders to deliver reliable, scalable solutions.

Design, build, and run data-heavy features for financial products, improve data models, pipelines, migrations, and multi-tenant systems. Work in a TypeScript monorepo, operate serverless AWS infrastructure, integrate LLM APIs, monitor production, and lead pragmatic technical decisions.

Build and ship LLM- and agent-powered product experiences for North, designing interfaces, evals, observability, and feedback loops to improve reliability and user outcomes. Collaborate with modelling, product, design, and customers to turn failures and feedback into measurable product and model improvements.

Build and own an internal AI-powered recruiting pipeline: end-to-end LLM-based sourcing, screening, AI interviews, evaluation logic, and deep integrations with ATS and scheduling to automate hiring workflows.

You will tackle AI challenges, develop scalable software and collaborate with teams to enhance healthcare navigation using AI solutions.

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

Build and own conversational voice AI products end-to-end: architect, implement, and deploy the platform. Design infrastructure, develop front-end and back-end systems, deliver reliable customer outcomes, and collaborate directly with the CTO to scale the product for enterprise customers.

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.

Design and deploy production-grade LLM-powered systems (RAG, agents, pipelines). Integrate AI across products, fine-tune and evaluate models, define prompt/modeling best practices, monitor model performance, and mentor engineers.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

The Staff AI Engineer will analyze AI quality, implement improvements, manage projects, collaborate with teams, and shape AI capabilities in a remote-first environment.

Lead architecture and implementation of autonomous inbound/outbound AI agents, build scalable LLM ops and signal-intelligence data pipelines, integrate identity resolution and third-party CRM systems, and maintain production-ready, well-documented systems for Series B diligence.

Fine-tune and align LLMs for specialized financial tasks; build agentic solutions with LangChain/Autogen; engineer ML pipelines for large financial datasets; deploy, monitor and scale production models; track AI research and integrate emerging tooling.

Design, build, and deploy production-grade AI solutions using LLMs, AI agents, vector databases, and cloud-native architectures. Integrate AI into underwriting and enterprise systems, implement prompt engineering, monitoring, governance, and operational best practices, and support scalable, secure AI services across the business.