Majd Arbash

Enterprise Solutions Architect at Amazon Web Services (AWS)

Old Toronto, Ontario, Canada
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Summary

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Majd Arbash is an Enterprise Solutions Architect with eight years of cloud-focused experience and a 15+ year engineering career building web platforms, teams, and resilient AWS architectures. He currently advises enterprise customers at AWS after leading cloud strategy and migrations as an AWS Solutions Architect and architecting a full platform transformation at SellAnyCar.com, including monolith-to-microservices, ECS/serverless, and infrastructure-as-code. Majd combines hands-on development and DevOps chops with people leadership—recruiting, scaling engineering teams, and introducing Agile and CI/CD practices to drive product and operational outcomes. He has deep practical experience integrating ML and vision services into production workflows (Amazon Rekognition, SageMaker, Google Vision) and has supported due diligence for a $35M investment round. Based in Old Toronto, he pairs pragmatic system-simplification with a strong bias for automation and measurable business impact.
code8 years of coding experience
job11 years of employment as a software developer
bookHigher National Diploma in Computing and Business Applications, Computing, Business, Higher National Diploma in Computing and Business Applications, Computing, Business at Edexcel International
bookBachelor of Science - BS, Computer Science (Informatics Engineering), Bachelor of Science - BS, Computer Science (Informatics Engineering) at Arab International University
languagesEnglish, Russian, Arabic
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7answers
1question
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Github Skills (34)

amazon-bedrock9
chatbot9
lamp8
aws-ecs8
aws8
retrieval-augmented-generation8
generative-ai7
amazon-ec27
serverless7
version-control6
bref6
activerecord6
blue-green-deployment6
php6
ai6

Programming languages (5)

TypeScriptJavaShellPHPPython

Github contributions (5)

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Contributions:19 commits, 10 pushes, 1 branch in 8 months
Generative AI Application Builder on AWS facilitates the development, rapid experimentation, and deployment of generative artificial intelligence (AI) applications without requiring deep experience in AI. The solution includes integrations with Amazon Bedrock and its included LLMs, such as Amazon Titan, and pre-built connectors for 3rd-party LLMs.
Contributions:31 pushes in 1 month
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