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Global Head of Engineering – AI‑Native Businesses & Platforms

Introduction

We are building a modern Wipro—one where AI‑native platforms and businesses become enduring engines of non-linear growth. We are creating a distinct AI‑Native Business & Platforms unit—built around product management, engineering, and forward‑deployed engineering—so we can modernize and scale what we already have, build new differentiating platforms, invest and partner where needed, and leverage Wipro’s distribution strength to take platforms global.

Purpose: Build secure, scalable, AI‑native platforms that can grow through consumption and support rapid field deployment.

Role Overview

• The Global Head of Engineering will lead the engineering charter for Wipro’s AI‑Native Businesses & Platforms unit.
• This role is responsible for building, modernizing, and scaling AI‑native platforms with the rigor, velocity, and reliability of world‑class product companies.
• Reporting into the CEO, AI-Native Business & Platforms Unit, this role owns platform engineering end‑to‑end, operating with a long‑term product engineering mindset while remaining deeply grounded in real client environments.
• You will lead all Platform Engineering Heads, shape a differentiated engineering culture, and ensure that Wipro’s platforms are built to scale, evolve, and endure—beyond individual engagements.

Key Responsibilities

• Own the engineering strategy, architecture (cloud‑native, modular, API‑first, multi‑tenant, AI‑ready), and execution across all AI‑native platforms and incubated businesses.
• Build and lead high‑performing global engineering teams responsible for core platform development, AI enablement, modernization, reliability, and security.
• Establish engineering standards for AI‑native architectures, agentic systems, data platforms, cloud‑native design, and compliance.
• Partner closely with Product Management and Forward‑Deployed Engineering to translate product intent into scalable, production‑grade platforms.
• Drive long‑term technical investments, balancing innovation, platform velocity, and operational stability.
• Develop senior engineering leaders and foster a culture of ownership, accountability, and continuous learning, aligned to the Spirit of Wipro.
• Modernize existing platforms to AI‑native (SLMs, agentic, knowledge graphs).
• Optimize cost per inference / transaction as usage grows — a critical capability for consumption‑based pricing.
• Enable FDE‑friendly extensibility (configuration > custom code). Build sandbox environments, plug‑in architectures, and tooling so field teams can extend platforms without touching core code
• Attract and retain top AI and platform engineers. Encourage IP creation through patenting programs.

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Capabilities Required

• Strong systems‑thinking and ability to engineer platforms that operate in complex, regulated, real‑world enterprise environments.
• Deep experience building and scaling enterprise AI-native platforms used by multiple customers.
• Credibility with senior engineers and executives alike; ability to lead with clarity, empathy, and decisiveness.
• Comfort operating in ambiguity and evolving problem spaces.

Prerequisites

• 15+ years of experience in product or platform engineering leadership.
• Background in product‑first / AI-native technology companies (AI platforms/software, enterprise software, data).
• Proven track record of building scalable engineering organizations and durable platforms.
• Strong technical foundation in Computer Science or Engineering.

Decision rights & interfaces:

• Final authority on tech stack, architecture, and coding standards.
• Joint planning with Product on feasibility and timelines
• Works with Security/CTO organization but retains product‑company speed.
• Strong interlock with FDEs vis-à-vis implementing, supporting, upgrading platforms
• Feedback loop with FDE for field‑discovered platform gaps / features required.

Success metrics (12–24 months):

• Platforms handle 3–5× usage growth without instability (99.9%+ availability).
• Reduced deployment effort per new client (measured in engineering‑weeks).
• Predictable release cadence (quarterly major updates delivered on schedule).
• Improved cost efficiency as consumption scales (declining cost per API call or per model inference).
• Attract & retain specialized product/platform engineering talent

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