Razorpay Appoints Ex-Google Engineering Leader Prabhu Rambadran as SVP, Steering an AI-First Future

Razorpay has appointed former Google engineering head Prabhu Rambadran as Senior Vice President of Engineering, as the fintech firm accelerates its AI-first strategy across payments and banking infrastructure.

A Landmark Leadership Hire

India’s fintech unicorn Razorpay has announced that Prabhu Rambadran — previously a senior engineering executive at Google LLC — is joining the company as Senior Vice President of Engineering. This appointment marks a key step in Razorpay’s strategic drive to embed artificial intelligence and deep engineering capability at the heart of its payments and fintech platform.

Rambadran’s arrival signals that Razorpay is preparing for its next growth chapter: moving from rapid payments scale-up toward infrastructure-led, AI-driven financial services across merchant banking, fintech and enterprise banking layers.

Background & Experience of Prabhu Rambadran

Prior to joining Razorpay, Rambadran held significant leadership roles at Google, where he led large engineering teams and global products. His experience spans cloud infrastructure, scalable systems and high-volume product engineering. Though full detail on his Google tenure is limited in the announcement, his deep technical credentials are well recognised.

By bringing in an engineering leader of this pedigree, Razorpay underscores its intention to lift the bar on its technology architecture, positioning itself as not just a payments provider but a full-stack fintech platform.

Razorpay’s AI-First Vision

Razorpay has publicly declared a shift toward an “AI-first” strategy across its stack. In recent months the company has rolled out initiatives such as:

  • Its internal AI assistant “Ray”, which reportedly handles approximately 70 % of customer queries across multiple languages.
  • Embedding AI into workflows across product, engineering, HR and legal teams, suggesting broad institutional adoption rather than pilot programs.
  • The RazorpayX platform, which uses AI-powered tools to provide automation and insights for CFO-office tasks like cash-flow forecasting, reconciliation and payroll.

In this context, the choice of Rambadran appears calibrated to drive architectural scale, build robust AI/engineering governance and underpin Razorpay’s shift from payments to financial infrastructure.

What Rambadran Will Lead

In his new role, Prabhu Rambadran is expected to oversee engineering for Razorpay’s core platforms, including payments, banking, merchant services and AI infrastructure. The appointment reflects both a scaling of team and ambition:

  • Upgrading the underlying payments stack to handle deeper AI-driven routing, fraud detection and decision-making flows.
  • Advancing the RazorpayX and banking verticals to support enterprise-grade banking services, deploying AI for back-office automation and financial workflows.
  • Strengthening the platform’s infrastructure for scale, reliability and internationalisation — key for a business targeting IPO and growth beyond India.
  • Fostering an engineering culture built for speed, quality, observability and AI-led innovation.

Strategic Implications for Razorpay

This move has a number of implications for Razorpay’s strategic positioning:

  1. Reinforcing technology as a competitive moat: As payments become commoditised, superior engineering and AI-capabilities can differentiate platforms.
  2. Shifting from payments to embedded banking & fintech: With engineering leadership of Rambadran’s scale, the company signals ambition beyond merchant payments toward banking primitives, API fintech services and global infrastructure.
  3. Strengthening IPO readiness: With Razorpay preparing for a potential listing, having recognised technical leadership helps bolster credibility with investors and global markets.
  4. Global ambition: A strong engineering hire indicates preparation for scale beyond India — in markets where performance, reliability and regulation demand enterprise-grade systems.

Broader Context in Indian Fintech

Razorpay is not alone in embedding AI deeply into its operations. Indian fintech firms are racing to transition from products to platforms. Key trends include:

  • AI-driven payments routing (e.g., Razorpay’s AI routing engine)
  • Fintechs preparing for IPOs emphasising infrastructure and science-backed growth rather than purely transaction volumes.
  • Rising importance of enterprise fintech (neobanking, CFO tooling, embedded finance) beyond consumer payments.

In this competitive backdrop, executive hires like Rambadran matter: they underpin credibility and capability when firms push infrastructure and growth claims.

Challenges & Considerations

While the hire is strong, execution risks remain:

  • Engineering scale-up: Building enterprise-grade AI systems at fintech scale is difficult—balancing agility, risk, reliability and regulation.
  • Talent retention: Maintaining engineering culture and staying ahead of global talent competition.
  • Regulation & risk: As AI plays larger roles (fraud detection, underwriting, routing), governance, bias and compliance become more critical.
  • Speed vs quality: The expectation for rapid innovation must be balanced with stability, especially with large merchant customers and consumer trust at stake.

Outlook

Over the next 12–24 months observers should watch for:

  • Announcements of major architecture or product rewrites led by the engineering team under Rambadran.
  • New AI-driven product launches beyond payments, such as banking services, CFO tools or underwriting platforms.
  • Engineering metrics and scale indicators (TPS volumes, uptime, latency, global expansion).
  • Talent moves and leadership hiring under the new engineering banner.
  • Impact on Razorpay’s IPO narrative and investor positioning as it emphasises deep tech and infrastructure.

If executed well, this hire could accelerate Razorpay’s transformation from India-centric payments player to global fintech infrastructure leader. The challenge will be translating leadership into velocity, reliability and product differentiation at scale.