Top AI Engineer Jobs in Denver & Boulder, CO

Lead the architecture and implementation of security controls for LLM applications, RAG systems, autonomous agents, and AI-enabled engineering workflows. Build AI security testing, red-teaming, monitoring, guardrails, automation, and secure development patterns. Establish technical direction, threat-model complex systems, secure model and tool access, respond to agent incidents, evaluate emerging AI technologies, and mentor engineers across security and platform teams.

Designs, builds, and deploys governed AI applications and data products in Snowflake. Leads RAG pipelines, agentic workflows, LLM applications, semantic layers, analytics models, evaluation frameworks, and user-facing AI experiences. Partners with business, engineering, security, and compliance teams in a HIPAA-regulated environment, ensuring data privacy, accuracy, scalability, and cost efficiency. Provides technical leadership, mentorship, stakeholder consultation, and guidance on AI architecture, governance, adoption, and emerging technologies.

Lead the design and delivery of production AI capabilities that coordinate workflows, retrieve information, invoke authorized tools, and connect directly to application state. Partner with Product, Design, engineers, customers, and domain experts to define workflows, evaluate quality, manage dependencies, improve reusable orchestration foundations, and ensure reliability, observability, secure rollout, and measurable customer outcomes.

Design, implement, and ensure quality of systems stack, tools, and SDK integration for AI frameworks. Collaborate with hardware and ML teams, apply engineering principles, write low-level/high-performance code, define architecture, estimate scope, and drive coding standards.

Develop AI models and algorithms for biomolecular modeling and simulation. Responsibilities include integrating large-scale molecular and laboratory data, writing research code, orchestrating data generation and training campaigns, developing deep learning architectures and generative models, publishing research, and collaborating with an interdisciplinary international team.

Own and evolve MAX Framework APIs and developer experience for AI inference and training. Design scalable APIs, programming models, distributed execution primitives, and developer-facing abstractions across Python and Mojo-adjacent surfaces. Lead RFCs and technical specifications, build reference implementations, establish API quality standards, and collaborate with compiler, runtime, kernels, serving, documentation, and DevRel teams.

Build and deploy a secure, offline document-intelligence system for RFQ screening. Responsibilities include PDF and drawing processing, OCR, multimodal model deployment, structured citation-grounded extraction, validation rules, audit logging, evaluation frameworks, and local versus Azure OpenAI comparisons. The role also includes packaging signed offline releases, documenting deployments, supporting onsite stakeholder sessions, and assisting client IT teams within strict CUI and air-gapped security boundaries.

Optimize end-to-end LLM inference performance on custom FPGA and ASIC accelerators. Responsibilities include profiling latency and throughput, tuning hardware-accelerated attention, optimizing distributed inference and KV cache data movement, building benchmarks and regression tests, and debugging across applications, runtimes, drivers, firmware, and hardware. The engineer will also influence accelerator architecture based on measured performance and support production deployments.

Build and operate production LLM systems using Ollama and llama.cpp, develop RAG pipelines and MCP integrations, and create Python backend services for AI features. Deploy and maintain workloads across Azure and AWS using Docker and CI/CD. Automate operations, process structured data, support Kafka-based architectures, troubleshoot across the stack, and progressively develop expertise in model quantization, GPU tuning, and secure offline deployments.

Build and deploy production AI features for an EHR platform, including LLM integrations, automations, data workflows, APIs, evaluations, monitoring, and HIPAA-compliant PHI handling. Partner directly with clinic customers and internal teams to prototype, implement, and harden solutions. Responsibilities include system design, clinical data integration, production debugging, AI tool reviews, and translating field insights into working software.

Designs and develops secure, performant Copilot products and platform services across Windows, Mac, and mobile. Builds distributed AI and agent-driven systems capable of reasoning, planning, and executing tasks. Collaborates with infrastructure, platform, application engineering, and research teams to ship high-quality software. Champions AI-assisted development, coding-agent adoption, prompt design, tooling best practices, and human-in-the-loop systems.

As an AI Process Forward Deployed Engineer, you will deploy enterprise workflows, improve systems, drive adoption, and collaborate across teams to enhance AI integration in business processes.

Develop and optimize GPU kernels, compilers, runtimes, and distributed inference systems for large language model serving. Improve latency, throughput, reliability, and hardware efficiency through profiling and low-level performance optimization. Integrate scalable improvements into production systems, collaborate across model, infrastructure, compiler, and hardware teams, and mentor engineers. Principal-level contributors additionally lead cross-stack initiatives and create reusable performance capabilities across models and hardware generations.

The AI Engineer designs, develops, and operationalizes responsible AI systems, including generative AI applications, RLHF models, guardrails, prompt engineering, and RAG frameworks. Responsibilities include evaluating and fine-tuning models, developing Python applications on cloud platforms, implementing ML software engineering practices and CI/CD pipelines, and collaborating with stakeholders on AI roadmaps and delivery. The role requires leading complex AI/ML initiatives and translating technical concepts for diverse audiences.

Leads research, design, development, validation, and deployment of AI/ML prototypes supporting space and missile defense. Collaborates with customers and multidisciplinary teams, translates mission requirements into technical solutions, mentors junior engineers, tracks milestones and budgets, supports proposals, and contributes to technical thought leadership.

Own production agentic AI systems that audit medical claims. Translate ambiguous business problems into measurable technical initiatives, define evaluation metrics, architect agent systems, build reliable Python software, and manage accuracy, latency, cost, and citation grounding. Lead technical direction, review designs, mentor engineers, collaborate with clinical and business stakeholders, and present defensible results. The role requires hands-on experience with agent SDKs, evaluation harnesses, long-context systems, and AI development tools.

The Principal AI/ML Engineer will lead the development of AI/ML platforms, drive technology architecture, ensure quality and integration, and provide strategic leadership while managing a team of engineers.

Build and operate production AI capabilities across enterprise platforms, including agent runtimes, model routing, RAG, tool calling, guardrails, evaluation, monitoring, and cost and latency optimization. Develop reliable, measurable, safe, and cost-conscious AI systems; create evaluation frameworks and quality controls; monitor production behavior; and collaborate with backend, platform, product, security, and client teams to deliver practical AI solutions.

Build, test, and support AI-powered applications and workflow automations using Microsoft Power Platform, GPTs, agents, APIs, and automation tools. Configure prompts, integrations, and knowledge sources; troubleshoot workflows; conduct testing and UAT; document solutions; support deployments and users; and follow information security practices. The role offers guided development for an early-career engineer with foundational programming, AI, automation, and software skills.

Architect, build, and deploy production-grade AI/ML platforms and LLM/RAG solutions. Lead system design, CI/CD and Terraform-based IaC, ensure reliability and monitoring, mentor engineers, run incident response, and prototype emerging AI technologies.

Build integrations that enable AI agents to interact with first-party and third-party applications. Develop structured agent actions, information retrieval interfaces, cross-application workflows, reusable SDKs, and reliable asynchronous APIs. Manage authentication, authorization, permissions, and integration limitations while collaborating with Android engineers on system-level application access.

Build agent workflows, orchestration, tool integrations, context retrieval, memory, multi-step execution, and long-running stateful processes. Improve reliability through evaluations and regression testing, integrate local and cloud model providers, and support model routing and inference optimization. The role requires backend or AI application engineering expertise, strong API and distributed systems knowledge, and proficiency in Python, Go, or TypeScript.

Evaluate, deploy, and optimize machine learning models for on-device execution on Android phones. Benchmark models on mobile hardware, optimize latency, memory, and power consumption, and apply quantization, compression, and distillation. Integrate models with Android applications, system services, and AI agent runtimes. Build hybrid local/cloud model routing and determine which workflows should run locally versus through cloud inference.

Build and scale AI agents for sales enablement across frontend and backend systems. Lead development with Next.js, React, TypeScript, Python, Postgres, OpenAI, and FastAPI; design robust AI integrations, engage customers, contribute to roadmap planning, and iterate rapidly in a high-ownership startup environment.

Researches and develops AI, machine learning, computer vision, robotics, signal processing, and control algorithms for autonomous defense and commercial systems. Builds and tests prototypes, trains and optimizes models, defines algorithm requirements, and collaborates across perception, planning, simulation, and testing teams. The role also includes developing MLOps demonstrations, proof-of-concept environments, technical presentations, customer discovery support, RFP responses, and sales engineering enablement.