Enterprise cloud transformation and data modernization for government and Fortune 500
RICEFW Technologies is a US-based IT services firm (201–500 employees, founded 2010) focused on cloud transformation, data platform modernization, and product development for government agencies and large private-sector clients. Their tech stack reveals a mature Windows/.NET enterprise shop (C#, ASP.NET, SQL Server, Power BI) with significant cloud infrastructure work (GCP, AWS, Kubernetes, multi-cloud deployment). Active hiring is engineering-heavy (6 of 9 roles) and skewed toward senior/director levels, paired with concurrent projects in CI/CD pipelines, infrastructure automation, and legacy report modernization—indicating scaled delivery operations and a technical customer base managing aging systems.
RICEFW Technologies develops IT product solutions and enterprise services tailored to mission-critical environments, serving government agencies and Fortune 500 organizations. Core offerings span product development and enhancement, enterprise cloud transformation, data platform modernization, robotic process automation, SAP services, and IT staff augmentation. The company operates from Okemos, Michigan, and runs a mature stack built around .NET/C# application development, SQL Server data infrastructure, and cloud platforms (AWS, GCP). Active delivery focuses on performance testing, CI/CD infrastructure, multi-cloud architecture, and modernization of legacy reporting systems—typical of large organizations managing complex technology estates.
C#, .NET, ASP.NET, SQL Server, Power BI, JavaScript, Angular, React, AWS, GCP, Kubernetes, Jenkins, Dynatrace, and security platforms including Palo Alto Networks and Fortinet.
Current projects include CI/CD pipeline integration and development, multi-cloud solution design, infrastructure automation, performance testing, Power BI dashboard development, and modernization of legacy reporting systems for operational visibility.
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RICEFW Technologies Inc'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 →
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