Top AI Engineer Jobs in Denver & Boulder, CO

Work across AI, backend, and frontend: curate and clean writing datasets, fine-tune large language models, run training experiments, develop metrics and eval sets, build backend services in Python/Django, and implement frontend features with React.

Design and operationalize enterprise AI governance capabilities across the AI lifecycle. Translate policies, privacy, security, risk, and compliance requirements into technical controls, guardrails, automated workflows, monitoring, observability, and audit evidence. Develop AI use-case registration, classification, taxonomy, risk assessment, and shadow-AI management capabilities. Partner with Privacy, Legal, Cybersecurity, Supply Chain, and Enterprise Architecture teams to implement responsible, secure, and compliant AI systems.

Design, build, and ship AI-powered observability features using LLMs and agentic workflows. Rapidly prototype, iterate with real users, integrate AI into alerting, runbooks, and developer workflows, and ensure solutions are scalable, maintainable, and production-ready while collaborating cross-functionally.

Integrate and fine-tune existing AI models within production software, evaluate model performance, build evaluation frameworks, configure observability, develop Python AI features, and troubleshoot real-world performance. Collaborate with data science, engineering, the CTO, and cross-functional teams to improve products and identify AI innovation opportunities. This role focuses on applying existing models rather than designing new machine learning models.

Senior AI/ML Engineer responsible for translating federal supply chain and logistics problems into analytical solutions, developing forecasting, classification, and NLP models, and deploying them into production. The role owns model development, data pipelines, monitoring, retraining, drift detection, validation, documentation, and stakeholder communication. Work includes operating within government-controlled environments and meeting federal bias evaluation and documentation requirements.

Designs, builds, deploys, and leads enterprise generative AI solutions, including LLM applications, chatbots, copilots, RAG pipelines, and automation assistants. Integrates AI with Azure, Databricks, EHR, FHIR, and HL7 data while meeting HIPAA, PHI, and governance requirements. Develops data pipelines, evaluates and fine-tunes models, guides architecture decisions, conducts code reviews, mentors team members, and collaborates with clinical, operational, data, and product stakeholders.

Own the technical design and delivery of multi-component AI systems, including LLM-backed features, agent tool orchestration, evaluation frameworks, data pipelines, and model harnesses. Build reliable APIs, SDKs, and feedback loops; optimize latency and cost; assess external models and datasets; and support production systems through on-call rotation. Mentor engineers and collaborate across search, location intelligence, and platform teams.

Direct autonomous coding agents on production work, review agent-generated pull requests, author evaluation suites, and own software quality across correctness, performance, security, and WCAG accessibility. The engineer will operate within a FedRAMP-authorized environment, maintain secure agent boundaries, advance autonomy based on reliability data, improve development lifecycle patterns, and mentor engineers adopting agent-directed delivery.

Senior technical leader responsible for architecting and building cloud-native, microservices-based platforms with AI capabilities. Lead design and delivery of scalable search and AI solutions (LLMs, RAG), data pipelines, search optimization (Elastic/OpenSearch/Solr), CI/CD and DevOps adoption, observability, and mentor engineering teams while driving responsible AI practices and stakeholder collaboration.

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.

Designs, develops, trains, tests, and deploys secure AI and machine learning solutions for enterprise and government environments. Responsibilities include preparing datasets, identifying automation opportunities, evaluating model performance and security, documenting risks and controls, and collaborating with data scientists, software developers, cybersecurity engineers, architects, and domain experts. The role applies AI and ML to enterprise security engineering, testing, and mission workflows.

Develop and deploy AI-native systems supporting legal and compliance operations. Build production RAG and agentic AI solutions, including retrieval, memory, tool use, multi-agent workflows, and evaluation frameworks. Partner with Legal, Compliance, and the MLRO to translate regulatory and operational needs into scalable tools. Conduct independent research, prototype solutions, benchmark system performance, and support compliance reviews, remediation tracking, product launches, and related operational activities.

Design, build, and deploy autonomous AI agents using LLMs and agent frameworks; integrate agents into e-commerce and supply chain systems; implement multi-agent orchestration, memory and tool integrations; fine-tune models and apply RL concepts; and build safety, monitoring, and MLOps pipelines.

Build production AI agents and agent-powered features from research and prototyping through deployment. Develop evaluation infrastructure with golden datasets, regression suites, and offline/online scoring. Own retrieval and context pipelines, production instrumentation, failure taxonomies, and cost and latency budgets. Partner with data science and product management on experiments and agent behavior, while establishing LLM engineering patterns, guardrails, and reusable tooling.

Build and deploy generative AI applications and multi-agent systems on Google Cloud using Vertex AI, Python, BigQuery, RAG, and secure enterprise data integrations. Develop data pipelines, train and serve models, evaluate AI output, protect sensitive information, troubleshoot production systems, and collaborate with technical and business stakeholders.

Build, maintain, and improve production AI services for document understanding including LLM pipelines, background jobs, databases, and internal tools. Implement features, debug production issues, write tests, and contribute to evaluation frameworks to improve model quality and system reliability. Collaborate with a small engineering team and take increasing ownership over platform components.

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.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Own applied AI research from problem framing through experimentation, evaluation, prototyping, and production deployment. Build agentic systems, evaluation platforms, model-serving solutions, and self-improving workflows across hosted and self-hosted models. Measure quality, cost, reliability, and speed; advise deployment teams; mentor engineers; and improve coding-agent practices.

Build and deploy agentic AI systems that automate customer experience operations, including reporting, QA analysis, case workflows, SLA risk detection, and knowledge maintenance. Develop orchestration layers, integrations, memory architecture, retrieval pipelines, and escalation logic. Measure AI reliability and business outcomes while partnering with data, QA, workforce management, and customer support teams. Productize successful tools for organization-wide scale.

Build and operate bare-metal AI and Kubernetes platforms, deploy and optimize large language models, and transition provisioning to Terraform- and Ansible-managed infrastructure. Responsibilities include OpenShift administration, platform observability, DNS and core services, cybersecurity compliance, feasibility assessments, documentation, and OCONUS hardware bootstrap support. The role requires strong Linux administration, AI model deployment, infrastructure-as-code, container platform, and DoD environment experience.

Design, build, and launch AI-focused software capabilities using Python and distributed-systems principles. Own initiatives from solutioning and architecture through production implementation and launch while remaining hands-on with code. Collaborate with Nextiva engineering and product teams, Google Gemini, and other external technology partners to extend AI capabilities across partner environments and the Nextiva ecosystem. Solve ambiguous early-stage technical problems and contribute to system design and architecture decisions.

Build and ship production-grade AI features by embedding with clinical, product, and operations teams. Own solutions end to end, including architecture, evaluation, validation, governance, deployment, monitoring, and human-in-the-loop safeguards. Develop agentic capabilities using tool calling, RAG, MCP integrations, and orchestration on AWS Bedrock. Establish acceptance criteria and risk controls, contribute reusable platform assets, and improve AI fluency across teams. The role requires strong Python, cloud engineering, full-stack development, and experience delivering validated AI features in production.

Build and operate production-quality AI framework, runtime, benchmarking, performance, automation, observability, and developer-tooling systems. Optimize large language model training and inference across GPUs and Microsoft hardware, diagnose cross-stack reliability and performance issues, and improve model onboarding, hardware utilization, Azure efficiency, and deployment speed. Senior engineers own major components and projects; Principal engineers define technical direction, architecture, and multi-release strategy while leading cross-team initiatives and mentoring technical leaders.

Leads enterprise AI-native engineering initiatives by researching and prototyping AI tools, designing AI-enhanced workflows, creating reference architectures, and developing proofs of concept. Owns AI training and enablement, including documentation, presentations, tutorials, demonstrations, and coaching. Evaluates tooling, supports adoption, communicates technical changes to leadership, hosts engineering office hours, and establishes scalable standards across the organization.