Top AI & Machine Learning Jobs in CO

Build production AI/ML software for supercomputers and AI infrastructure, including anomaly detection, forecasting, root cause analysis, automation, and operational intelligence. Develop services with Go, Python, and React; integrate machine learning models, LLM workflows, agentic systems, and analytics. Own the lifecycle from data engineering and model development through deployment, monitoring, and improvement while collaborating with engineering, architecture, product, and customers.

Leads complex AI and cloud-native incubation projects, designing and implementing scalable services across public cloud, datacenter, and edge environments. Develops Generative AI, microservices, and distributed-system solutions; evaluates emerging technologies through prototypes and research spikes; guides architecture, security, reliability, testing, CI/CD, and observability. Leads small multidisciplinary engineering teams, collaborates with stakeholders, mentors engineers, and advances innovative capabilities across multiple parallel projects.

Designs and owns LLM inference runtime components, including engine integration, continuous batching, KV cache management, quantized execution, distributed execution, and Kubernetes orchestration. Improves latency, throughput, GPU utilization, and autoscaling across enterprise deployments. Evaluates emerging inference technologies, resolves customer issues, contributes to architecture and code reviews, mentors engineers, and leads engineering practices. Requires expertise in LLM serving, Kubernetes, Go, Python, C++/CUDA, GPU systems, and production model serving.

Build production AI and machine learning software for large-scale computing and AI infrastructure. Responsibilities include developing anomaly detection, forecasting, root cause analysis, LLM workflows, agentic systems, and operational analytics using Go, Python, and React. The role spans data engineering, model development, evaluation, deployment, monitoring, and continuous improvement while collaborating with engineering, architecture, product, and customer teams.

Develop AI data infrastructure, metadata and lineage systems, foundation models, agentic AI platforms, and knowledge-infused learning capabilities. Build and evaluate NLP, LLM, time-series, diffusion, and computer vision systems while improving reproducibility, governance, reasoning, scalability, and trustworthy AI outcomes. Partner with software teams and external collaborators to translate innovative research into production implementations.

Build and implement AI-powered developer productivity tools, including code review automations, AI personas, reusable prompts, and workflow playbooks. Partner with engineering teams to identify coding workflow improvements, prototype tools in Python, integrate GitHub and CI/CD systems, measure adoption and quality impact, and support enablement through demonstrations, office hours, and training.

Develops research methods for context discovery, retrieval, multimodal representation, reasoning, tool calling, metadata management, and trustworthy AI data pipelines. Builds and evaluates foundation models across language, vision, time series, and scientific applications. The role combines applied research with hands-on engineering, including MLOps, agentic AI, knowledge-infused learning, pipeline optimization, and collaboration with software teams and external partners.

Lead customer-facing architecture for AI data platforms and AI Factory environments. Design data-centric solutions for RAG, inference, and training workflows; size GPU and storage environments; optimize data movement, retrieval, latency, throughput, cost, and reliability. Partner with customers, sales, and Product Management to qualify opportunities, influence product roadmaps, create reference architectures, and develop scalable go-to-market solutions.

Lead the architecture and technical direction of HPE’s LLM inference runtime and Kubernetes orchestration platform. Own engine integration, continuous batching, KV cache management, quantization, distributed inference, GPU scheduling, autoscaling, and performance optimization. Evaluate emerging inference technologies, mentor engineers, lead architecture reviews, and communicate technical strategy to executives. The role targets low latency, high throughput, and efficient GPU utilization across enterprise, air-gapped, and sovereign environments.

Own product areas within HPC/AI system management and orchestration software: define requirements, prioritize features, create PRDs and user stories, collaborate with architects and engineering, engage customers, support releases and go-to-market, and monitor product performance to drive improvements.