Razorpay hires engineering leaders from Microsoft, Salesforce, CRED, Divyam.ai to bolster AI push
04 Aug 2026, 04:16 PMAs part of the team expansion, former Divyam.ai CTO and co-founder Sudhir Reddy has joined Razorpay to lead its AI and data architecture initiatives.
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Fintech major Razorpay has strengthened its artificial intelligence and engineering leadership team with the appointment of four senior leaders from Microsoft, Salesforce, CRED and a young AI startup Divyam.ai.
The move is aimed at accelerating the company’s efforts to build AI-native financial infrastructure and prepare for the rise of agent-driven commerce, a statement said.
As part of the team expansion, former Divyam.ai CTO and co-founder Sudhir Reddy has joined Razorpay to lead its AI and data architecture initiatives. He brings nearly two decades of experience in building large-scale AI and enterprise technology systems, having previously held leadership roles at Divyam.ai, Flipkart, Yahoo and Symantec.
The company has also appointed Abhishek Agarwal, formerly a Principal Group Engineering Manager at Microsoft; Bhavya Shivaprakash, previously a Senior Director at Salesforce; and Anuj Mathur, who earlier served as Senior Director at CRED. Together, the executives bring expertise across artificial intelligence, cloud infrastructure, developer platforms and data engineering.
The new leadership team will focus on enhancing AI capabilities across payments, banking, risk management, data systems and developer platforms. The company aims to create more autonomous and scalable financial systems that can support business decision-making in addition to processing transactions.
The appointments, Razorpay added, build on its broader AI push that began with the onboarding of Praburam Rambadran as Senior Vice President of Engineering in September 2025. Since then, the company has launched products including Agentic Payments, Agent Studio, Agentic Dashboard and Connected Banking Agents, while expanding its suite of AI-powered internal tools.
"Getting AI into production at financial scale is a hard, unglamorous problem - it's about data quality, system reliability, and judgment under real constraints, not just model capability. That's why we hired for depth: people who've actually shipped AI in high-stakes environments and know where it breaks," said Rambadran.
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