Senior Principal Engineer, AI Transformation

PLT Engineering; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

Grab is seeking a senior technical leader to transform how we engineer with AI, raise the architectural bar for large-scale distributed systems, and create common platforms and patterns that allow teams to move faster with greater autonomy. You will serve as an architectural leader with an organisation-wide mandate, enabling autonomous teams while maintaining coherence across CXI and Grab's ecosystem.

The role focuses on AI-native engineering, scalable platforms, and cross-functional

Responsibilities

• Transform the Software Development Lifecycle with AI.
• Architect Systems for Massive Scale and Reliability.
• Build Common Platforms Across the Grab Ecosystem.
• Enable Autonomous Teams While Maintaining Architectural Coherence.

Job description

Get to Know the Team

Grab operates one of Southeast Asia's most complex digital ecosystems, connecting millions of consumers, drivers, merchants, and partners across mobility, food delivery, e-commerce, financial services, and digital products.

The Consumer Experience & Integrity (CXI) organisation cuts across this ecosystem, spanning Trust, Safety, Identity, SuperApp architecture, Loyalty & Subscriptions, Agentic Customer Support, Communications, Home Page Personalisation, and Growth.

Get to Know the Role

We are looking for a senior technical leader—a strong individual contributor with an organisation-wide mandate: transform how we engineer with AI, raise the architectural bar for large-scale distributed systems, and create common platforms and patterns that allow teams to move faster with greater autonomy.

The Critical Tasks You Will Perform

1. Transform the Software Development Lifecycle with AI

AI is fundamentally changing how software is designed, built, tested, operated, and evolved. You will help CXI move beyond isolated AI coding tools toward an AI-native engineering model.

Shape the architecture and engineering practices needed to embed AI and autonomous agents throughout the SDLC—from requirements and design through implementation, testing, deployment, operations, and incident response.

Establish the platforms, context and knowledge systems, evaluation frameworks, testing strategies, and guardrails that make higher-autonomy engineering safe and scalable. The goal is to enable teams to operate with greater autonomy, faster feedback loops, stronger quality, and significantly higher engineering leverage.

2. Architect Systems for Massive Scale and Reliability

Provide architectural leadership for high-throughput, highly available distributed systems powering critical consumer and integrity experiences.

Help teams solve difficult problems around scalability, reliability, resilience, performance, security, and cost, while establishing architectural patterns that prevent systemic failures rather than solving reliability one incident at a time.

3. Build Common Platforms Across the Grab Ecosystem

CXI spans Integrity—Trust, Safety, and Identity as well as SuperApp architecture, Loyalty & Subscriptions, Agentic Customer Support, Communications, Personalisation, and Growth.

Identify where capabilities should remain domain-specific and where they should become common platform primitives, creating leverage across businesses including mobility, food delivery, e-commerce, financial services, and digital products.

The goal is simple: solve difficult platform problems once and enable many teams and businesses to build on top of them.

4. Enable Autonomous Teams While Maintaining Architectural Coherence

Serve as a technical architect and force multiplier across CXI, partnering with senior engineers, engineering leaders, product leaders, and data scientists.

Establish reference architectures, engineering principles, reusable patterns, and technical guardrails that allow teams to make more decisions independently. For the hardest cross-cutting problems, go deeper to help teams evaluate trade-offs and converge on architectures that work beyond a single use case.

Success means teams become more autonomous because of the architecture, not more dependent on the architect.