AVP/VP, AI Engineer, Technology Group

About GIC GIC is one of the world’s largest sovereign wealth funds. With over 2,000 employees across 11 locations around the world, we invest in more than 40 countries globally across asset classes and businesses. Working at GIC gives you exposure to an extraordinary network of the world’s industry leaders. As a leading global long-term investor, we work at the Point of Impact for Singapore’s financial future, and the communities we invest in worldwide.

Technology Group We experiment, design, and lead a 24×7 global business where we support core capabilities in asset management, trading, investment operations, and risk management. We deliver secure, reliable, and integrated solutions, and provide insights on new and emerging technologies.

Strategy, Architecture, and Transformation Group The Strategy, Architecture & Transformation (SAT) group shapes and drives GIC’s technology strategy, ensuring alignment with business priorities and enterprise goals. Bringing together expertise in strategy, architecture, engineering, and transformation, the team strengthens governance, promotes consistency, and accelerates delivery across the Technology Group. Through modern practices and close collaboration, SAT leads the development of an architectural strategy that reinforces oversight and accountability while enabling reliable, scalable solutions and informed decision making across the Technology Group and, more broadly, across GIC.

AI Engineering The AI Engineering team within SAT is driving GIC’s transformation from AI-enabled to AI-native. We build and operate the foundational AI platform — gateway, agent runtime, agentic IAM, memory, observability, and more — so that every team across GIC can develop and deploy AI agents that are secure, observable, and production-grade.

What impact can you make in this role? As a Senior AI Engineer on the core platform team, you will be the technical anchor for the most complex problems in the stack — how agents run, scale, fail, recover, and compose in production. You will own the architecture and technical direction of the agent runtime, orchestrating multi-step workflows, managing durable state, scaling agent workloads, and connecting agents to tools, data, and other agents.

You will make pragmatic technology choices — knowing when to adopt a framework, when to extend one, and when to build an abstraction layer that keeps the team from getting locked in. You are a platform engineer who enables dozens of teams to run hundreds of agents reliably, securely, and at enterprise scale.

• Own the agent runtime architecture — design and evolve the execution layer, including lifecycle management, workflow orchestration, durable execution (Temporal), state checkpointing, and compute scheduling
• Scale agentic workflows in production — solve the hard problems of running agents at scale: long-running workflows, parallelism, delegation, backpressure, rate limiting, and graceful degradation under load
• Drive technical direction across the platform — lead architecture decisions for distributed-systems concerns such as state management, event-driven communication, context engineering, multi-agent coordination, and protocol selection (MCP, A2A)
• Build for resilience from day one — embed reliability patterns like circuit breakers, retries with jitter, dead-letter queues, idempotent operations, and chaos-testable interfaces
• Shape the agent framework strategy — evaluate, adopt, and extend frameworks (LangGraph, CrewAI, OpenAI Agents SDK, Semantic Kernel) with clear trade-offs
• Architect the memory and state layer — design boundaries between runtime state and durable agent memory to ensure agents can remember, resume, and improve across sessions
• Harden the platform as we build it — embed production-readiness from the first sprint: structured logging, distributed tracing, health checks, deployment safety, and operational runbooks
• Mentor and raise the engineering bar — set technical standards for code review, system design, testing strategy, and documentation; make the team better through reviews and pairing

What will you do as an AI Engineer? You will design and evolve the agent runtime — the execution layer that orchestrates multi-step workflows, manages durable state, scales workloads, and connects agents to tools and other agents. You will work across all platform capabilities (gateway, IAM, memory, observability) because the runtime t…