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

As a Staff NLP Scientist, you will solve complex medical document understanding problems using NLP and Computer Vision, optimizing models for performance and scalability.

The NLP Scientist will solve complex medical document understanding problems using NLP and CV methodologies, optimize models for performance, and make data-driven decisions.

The AI Engineer will initially function as a Full-Stack Engineer, progressing to integrate AI solutions into products, enhance efficiency, and shape AI application at Sharebite.

Lead architecture and delivery of high-throughput transactional services and agentic AI workflows. Build production Python and Go microservices on AWS, design observability and CI/CD, resolve distributed-system performance issues, run technical reviews and ADRs, mentor engineers, and contribute to on-call and incident response.

Design, build, and operate production multi-agent AI systems and automation for Marketing Operations. Integrate LLMs with business data (BigQuery, CRMs, Slack), build MCP servers, APIs, and scalable serverless/containerized services, implement observability and governance, and partner with GTM teams to deliver measurable automation and self-service workflows.

Design, build, and deploy production-ready AI solutions: data pipelines, dataset preparation, model training/tuning (including LLMs, embeddings, RAG), model optimization, and integration into enterprise systems while ensuring data security, governance, and collaboration with stakeholders.

Support development and evaluation of AI features and RAG pipelines: prompt experimentation, data preparation, embedding generation, model evaluation, debugging, and basic AI service development using Python and ML tools under senior engineer guidance.

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

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.

The Data & AI Engineer will develop data pipelines, predictive models, and dashboards while collaborating with software teams to integrate insights into engineering processes.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

Lead development of foundation models, generative AI, and agentic systems for MEP design workflows. Build scalable data pipelines for BIM/CAD and multi-modal geometric datasets, create evaluation and guardrails, translate research into production-grade systems, mentor engineers, and drive technical direction and cross-functional collaboration.

Join the AI Transformation team to co-develop and productionize the Elixir AI platform: implement runtimes, retrieval/memory, multi-model routing, observability, secure multi-tenant features, event-driven agents, and productize FDE-built solutions while partnering with stakeholders and shipping high-quality production software.

Design, build, and own production AI systems and MCP infrastructure for LLM-powered assistants, agentic workflows, and threat detection. Lead API/service design, cloud infrastructure, security and safety for multi-tenant systems, tune performance and observability at scale, define eval-driven AI quality benchmarks, and mentor engineers.

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 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.

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

As a Senior AI Controls Solutions Engineer, you'll operationalize Phaidra's AI solutions for enterprise data center customers, collaborating with teams to model facilities and define product requirements, applying deep knowledge of control systems and AI technologies.

Develop large-scale Foundation Models for Autonomous Driving & Robotics, collaborating with teams, optimizing model performance, and mentoring junior members.

As a Staff Software Engineer, you'll develop an AI-first platform, design cloud-native APIs, and collaborate on Site Builder experiences with a focus on scalability and quality code.

Design, develop, and maintain scalable CPQ solutions on Salesforce. Implement Apex and Lightning components, optimize existing systems, and mentor junior developers.

As a Staff AI Engineer, you will develop backend services, integrate APIs, and optimize existing systems within a complex AI platform environment, collaborating with cross-functional teams and ensuring high reliability and performance.

The Senior AI Engineer leads software implementation, mentors engineers, collaborates with product and DevOps teams, and ensures production reliability.

Lead and deliver an enterprise AI platform and agent factory: design architecture, build cost-effective model infrastructure, harden security/SSO/IAM, enable developer adoption, and build agent workflows across the PDLC (ideation, development, deployment). Mentor engineers, steer delivery partners, and remain hands-on implementing agents, RAG pipelines, integrations, and observability to drive measurable productivity gains.