AI and engineering delivery firm for government, banking and enterprise
CodeNinja is a Dallas-based AI and software delivery firm running engineering teams across the US, Saudi Arabia and Pakistan. Its stack pairs Microsoft Dynamics 365 and the Power Platform with Java/Spring, .NET and a Python ML toolkit on AWS, and job posts point to a Saudi banking CRM modernization tying D365 to core banking systems. Hiring is accelerating and senior-weighted, with Bedrock, Azure and GCP being adopted alongside AWS.
Notable leadership hires: CVM Segmentation Head, Development Lead
CodeNinja Inc. is a privately held company founded in 2014 and headquartered in Dallas, Texas, with 201–500 employees. It designs, builds and operates AI and software systems for enterprises, software acquirers and government and defense clients, including workflow automation, AI/ML infrastructure and embedded engineering teams. Engagements range from fixed-scope projects and AI pilots to managed teams and build-operate-transfer Global Capability Centers. The company maintains offices in Dallas, San Francisco, Riyadh and Lahore, and currently recruits in Saudi Arabia and Pakistan.
CodeNinja works with Microsoft Dynamics 365, Power Apps, Power BI, Dataverse, .NET, Java with Spring Boot, React, Python ML libraries like scikit-learn and XGBoost, and AWS with Terraform and CDK.
CodeNinja's current hiring is focused on Saudi Arabia and Pakistan, matching its offices in Riyadh and Lahore, with headquarters in Dallas, Texas.
Job posts point to a Saudi National Bank CRM modernization, Dynamics 365 integration with core banking systems, D365 data migration, bank-wide resilience work and AWS landing zones.
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CodeNinja 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 →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.