Top AI & Machine Learning Jobs in CO

Bridge AI models and field operations to deploy practical AI solutions: prepare data governance, integrate enterprise chatbots, enable wearable AI and AR onboarding, build agentic "AI manager" workflows, and optimize prompts, procedures, and automation to improve technician productivity and service quality.

Design and deploy production-grade ML models and scalable GenAI/LLM solutions to optimize customer journeys and enterprise workflows. Build cloud-native data science pipelines, run experiments/A-B tests, translate insights into dashboards, and mentor peers on MLOps and data storytelling.

Build scalable Databricks/Spark data pipelines, implement automated GitLab CI/CD workflows, apply SOLID engineering and testing to ensure pipeline reliability, integrate AI/ML and NLP tools, mentor peers, and operationalize models in AWS production environments.

Manage the human validation layer for AI predictive models, govern ethics and bias mitigation, design orchestration workflows to trigger personalized micro-learning, monitor model drift, translate insights to stakeholders, and measure AI-driven training effectiveness for frontline agents.

The Senior AI Platform Engineer will lead technical onboarding to the Databricks platform, connect data sources, and implement governance and cost management for AI use cases.

The role involves engineering self-healing AI systems with high observability, developing RAG pipelines, and automating incident resolution using Python and Ansible.

Lead strategy and roadmap for IVR and chat conversational AI, deploy and optimize generative AI agents, curate training data, build performance dashboards, ensure governance with legal/compliance/UX, and run analytics-driven A/B experiments to improve containment and accuracy.

The AI Platform Engineer II will manage Databricks environments, implement automation and governance for AI infrastructure, and ensure platform reliability.

Lead enterprise AI and architecture strategy to modernize OSS/BSS and cloud-native systems. Design, train, and deploy ML models and autonomous agent frameworks, establish TOGAF/TM Forum governance and reference architectures, define OSS/BSS roadmaps, and collaborate with cross-functional teams to standardize architecture patterns and rationalize technology portfolios.

Serve as the bridge between AI models and field operations to deliver measurable business value. Prepare data for AI readiness and governance, implement and iterate a knowledge-base chatbot, pilot wearable AI and AR onboarding for technicians, build an "AI manager" with agentic workflows, and standardize prompts, automation, and workflows to optimize technician productivity and service quality.