Top AI Jobs in Denver & Boulder, CO

Designs and operates production-quality AI framework, runtime, benchmarking, performance, automation, and observability components. Benchmarks, profiles, debugs, and optimizes large language model training and inference across GPUs and Microsoft hardware. The role drives projects from definition through deployment, investigates cross-stack reliability issues, partners with research, infrastructure, model, and hardware teams, contributes to technical standards, and mentors engineers.

Leads trust and safety for AI products serving teachers and students. Owns model guardrails, evaluations, red-teaming, monitoring, incident response, student-facing safety, district trust documentation, and regulatory alignment. Builds safety infrastructure into product development, manages incidents and postmortems, partners with Legal and Engineering, and leads a growing Trust & Safety team.

Own the architecture and implementation of Life360’s cross-functional AI agent ecosystem. Define reusable agent standards, identity and authorization models, evaluation and regression infrastructure, execution frameworks, retrieval systems, telemetry, and cost controls. Build production components, guide build-versus-buy decisions, establish shared architecture and standards, validate systems through live use cases, and mentor senior engineers across AI Platforms, AI DevX, and Agentic PDLC.

Provides technical leadership for an Engineering Generative AI portfolio. Defines architectures, roadmaps, reusable patterns, and secure full-stack solutions spanning applications, APIs, services, data, AI models, and deployment pipelines. Guides prototypes into enterprise-ready systems, evaluates emerging technologies, embeds responsible AI governance, and designs human-centered agentic workflows. Partners across engineering, IT, data, cybersecurity, vendors, and leadership while mentoring technical teams.

Lead technical vision and architecture for AI frameworks, performance, benchmarking, and tooling. Drive cross-team alignment, mentor engineers, ship high-performance distributed software, and improve model onboarding, runtime performance, reliability, and Azure hardware efficiency.

Leads architecture, development, and delivery of secure cloud, data, process intelligence, and AI solutions for federal clients. Builds AWS cloud-native services, scalable data platforms, Celonis process-mining solutions, and agentic AI workflows using Amazon Bedrock and Azure OpenAI. Owns technical direction, engineering standards, security, testing, observability, deployment automation, and reusable product packaging. Supports A-123 continuous controls monitoring, agency implementations, demonstrations, and cloud environment deployments while collaborating with AWS and Celonis partners.

Lead development of the EisnerAI Platform by designing technical architecture across data, governance, AI, agents, and application layers. Build agentic AI solutions, develop model evaluation and post-training optimization frameworks, and oversee custom LLM and AI agent implementations. Ensure responsible AI practices, data governance, integration, state management, logging, and error handling across enterprise-scale Azure solutions.

Develop and maintain production AI-powered software for mission-critical USPS systems. Design distributed, microservice-based architectures, improve reliability and scalability, identify bottlenecks, and modernize existing systems. Work across the technology stack, communicate architectural decisions, mentor junior developers, and use AI and automation tools to improve engineering outcomes. The role is remote and requires the ability to obtain Public Trust clearance.

Leads RK Industries’ enterprise AI technical team, roadmap, and delivery lifecycle. Responsibilities include hiring and managing AI engineers, designing Microsoft Foundry and Copilot Studio agents, building enterprise RAG solutions, integrating governed Fabric and MDP data, establishing evaluation and monitoring standards, and implementing responsible AI controls. The role partners with business leaders to identify use cases, define requirements and success measures, oversee external partners, prototype hands-on in Python, C#, or SQL, and transition AI solutions into secure, production-ready deployments.

Build and own Life360’s AI platform infrastructure, including agent orchestration, MCP gateways, LLM routing, evaluation systems, observability, memory, retrieval, and production inference. Establish secure, reliable, cost-effective shared AI capabilities; define standards, SDKs, and paved paths for engineering teams; collaborate with Security, Privacy, Legal, and AI-focused teams; respond to incidents; and mentor engineers on agent architecture and evaluation practices.

Build and own Supabase’s AI tooling, including agents, MCP servers, agent skills, dashboard assistants, and developer-facing AI capabilities. Establish eval-first development through benchmarks, instrumentation, quality KPIs, and regression gates. Design how documentation and product knowledge are exposed to agents, create reusable AI patterns, and collaborate across Docs, CLI, Frontend, SDK, and infrastructure teams. Improve reliability, testing, authentication, hosting, and developer experience in a fast-moving, globally distributed environment.

Develop AI agents, applications, dashboards, websites, and automated workflows for telecommunications engineering. Integrate AI solutions with databases, APIs, GIS platforms, and engineering systems to automate OSP documentation, project tracking, validation, reporting, fiber network planning, permitting, and construction documentation. Collaborate with engineering, field operations, and IT teams, train employees on AI tools, and ensure solution accuracy, security, and reliability.

Evaluate and improve coding agents by assigning real software engineering tasks, reviewing model-generated work, completing real coding problems, and identifying model failures. The role is a fixed-pay contract offering $6,000 per week for 40 hours, with flexible scheduling and ongoing weekly work. Candidates need at least four years of professional software engineering experience, a computer science or related degree, U.S. work authorization, strong written English, and availability for 40 hours weekly.

Leads teams developing, integrating, and deploying AI/ML capabilities for radar, communications, electronic warfare, and other sensor-level applications. Establishes technical roadmaps, manages staffing and workloads, mentors engineers, supports proposals and customer engagements, and collaborates with program, systems engineering, software, and customer teams. The role requires expertise in machine learning, signal processing, sensing systems, mission-critical software integration, and technical leadership.

Lead the architecture, development, evaluation, and operation of enterprise autonomous AI agents integrated with Salesforce. Build stateful multi-agent workflows using LangGraph, Claude, and MCP; implement evaluation, observability, security, and PII protection; optimize model routing and performance; and establish engineering standards, API contracts, and architecture roadmaps. The role requires extensive software engineering and production LLM experience, strong debugging and stakeholder communication skills, and advanced English proficiency.

Identifies business opportunities for AI, develops prototypes and production-ready solutions, and manages the full lifecycle from discovery through deployment and continuous improvement. Partners with business and technical teams to translate operational challenges into scalable solutions, while ensuring governance, security, privacy, compliance, auditability, and responsible AI practices. Supports adoption, training, performance monitoring, and the development of reusable AI frameworks and standards.

Leads customer discovery and end-to-end deployment of Workday AI agents in enterprise environments. Configures, debugs, and improves solutions using customer data and workflows; manages Early Access customers and executive stakeholders; triages issues; translates feedback into product improvements; defines graduation criteria; and creates deployment playbooks and documentation. Requires deep Workday knowledge, enterprise implementation experience, entrepreneurial ownership, and travel to customer sites up to 20%.

Leads AI/ML architecture strategy for customers, designing production-grade solutions involving RAG, agentic systems, guardrails, evaluation, and observability. Advises senior technical stakeholders, drives Databricks adoption and consumption, leads competitive technical engagements, coordinates cross-functional resources, and influences product direction. Requires strong Python and SQL skills, distributed data architecture expertise, public-cloud deployment experience, and willingness to travel up to 30%.

Develop and enhance enterprise AI agents, orchestration workflows, conversational applications, and AI-powered automation using Python, LangGraph, and LangChain. Integrate AI solutions with enterprise systems and APIs, implement planning, memory, tool-calling, observability, and monitoring capabilities, and support production-grade generative AI platforms. Collaborate with engineering teams and stakeholders on architecture, reliability, performance, testing, and scalable AI engineering practices.

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, and end-to-end ML pipelines. Define AI/ML use cases, transition roadmaps, cognitive adoption metrics, and data-mining strategies. Participate in SAFe Agile delivery, write user stories, remove technical impediments, support DevSecOps integration, and produce transition and knowledge-transfer materials.

Define and maintain the company’s AI architecture, standards, and roadmap for agentic systems. Identify and prioritize enterprise AI use cases, guide platform and buy-versus-build decisions, and establish safe, scalable implementation patterns. Partner with R&D, Medical, Commercial, and G&A teams while mentoring AI engineers and stakeholders. Oversee AI infrastructure, technology evaluation, and FinOps optimization for compute and token usage. Travel internationally approximately 25–40% of the time.

Design, build, and maintain scalable full-stack applications; collaborate with cross-functional teams; integrate AI and agentic systems to enhance product features; ensure performance, fix bugs, and resolve bottlenecks.

Design, implement, and operate production-quality AI framework components, benchmark and optimize large model training and inference across GPUs and Microsoft hardware, build automation and observability, drive cross-team projects from definition to deployment, and provide technical leadership and mentorship for improvements in performance, reliability, and model onboarding velocity.

Leads engineering teams responsible for Blackbaud’s enterprise Data and AI platform. Owns lakehouse architecture, ingestion, transformation, feature services, ML enablement, reliability, governance, security, cloud migration, and cost optimization. Establishes technical strategy, roadmaps, metrics, and operational standards while partnering with Product, Data Science, Analytics, Architecture, and Security. Hires, mentors, and develops platform engineers and influences data and AI engineering practices across the organization.

Build and operate the inference and orchestration backend powering AI interactions across mobile and desktop applications. Responsibilities include designing production AI services, optimizing latency and throughput, managing caching, batching and streaming, and maintaining monitoring, logging, alerting, and incident response systems. The role requires reliable APIs, distributed-systems debugging, and scalable integration with LLMs, embeddings, and multimodal models.