H2O.ai builds an enterprise AI platform spanning predictive models, LLM-powered apps and agentic systems, running on a Kubernetes, Go, gRPC and Helm core. Pain points around FedRAMP compliance and air-gapped deployment, plus NVIDIA, Dell and VAST Data in the stack, point to a push into regulated, on-prem and public-sector buyers. Hiring is accelerating and engineering-heavy, with roles spread across the US, Sri Lanka, Singapore, Canada and Australia.
H2O.ai is a privately held AI software company founded in 2012 and headquartered in Mountain View, California, with 201–500 employees. It develops open-source and commercial technology that lets enterprises and public sector agencies build generative and predictive AI applications on their own data. Deployments emphasize data privacy and infrastructure flexibility, running across AWS, GCP, Azure and on-premises hardware. The company operates internationally, with hiring activity in North America, Asia-Pacific and Sri Lanka.
H2O.ai runs on Kubernetes, Go, Python, Java, C++, gRPC, Protocol Buffers, Kafka, Helm and Terraform, across AWS, GCP and Azure, with NVIDIA, Dell and VAST Data infrastructure.
Job posts point to LLM-powered applications, agentic AI systems, a platform for deploying and monitoring ML models, and a Kubernetes-based platform for AI engine lifecycle management.
H2O.ai is hiring in the United States, Sri Lanka, Singapore, Canada and Australia, with engineering making up most open roles.
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H2O.ai's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.
