Lead AI Analytics Engineer - CyberOrg Integrity

Company Description

About Grab and Our Workplace

Grab is Southeast Asia's leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, we've got your back with everything. In Grab, purpose gives us joy and habits build excellence, while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility.

Job Description

Get to Know the Team 

The Integrity team is dedicated to Trust, Identity, and Safety, safeguarding our users & transactions on Grab. We leverage our expansive datasets to address critical challenges, protecting millions of users from sophisticated fraud, systemic identity abuse, and severe safety violations. Today, we are undertaking an aggressive transformation to become a "CybOrg"—moving away from manual data analysis towards an autonomous state where highly resilient, autonomous agentic workflows redefine how we detect and mitigate risk at scale. We rely on our technical builders to architect the advanced data infrastructure and orchestration layers that give our AI agents their "brains" and keep Grab ahead of evolving threats.

Get to Know the Role 

Reporting to the Head of Analytics for Risk, you will serve as the critical "Agentic Middleware" layer for the Integrity team. This is not a traditional analytics or engineer role; you will be the technical architect connecting our Trust & Safety business logic to our real-time Risk Engine. You will help the team to build the data pipelines, vector databases, and multi-agent orchestration frameworks that power our AI. You will solve deep backend technical bottlenecks, build proactive guardrails, and ensure our autonomous systems can operate reliably and accurately without breaking core production systems.

The Critical Tasks You Will Perform

• Architect the AI Data Infrastructure: Build and optimize the data ingestion pipelines behind our AI, including vector databases, embeddings generation, and parsing unstructured data (such as Slack logs and historical investigations) to enrich the agent's knowledge base.
• Orchestrate Multi-Agent Workflows: Design, deploy, and maintain end-to-end multi-agent systems using modern orchestration frameworks (e.g., LangChain, LangSmith, LangGraph) to automate complex risk detection workflows.
• Build Proactive Blast-Radius Controls: Implement automated, code-level intercepts (such as Abstract Syntax Tree (AST) parsers and EXPLAIN query cost evaluations) to block hallucinated or destructive AI-generated SQL/rules before they hit production.
• Develop Secure Deployment Guardrails: Build tenant-owned Model Context Protocol (MCP) servers and highly restricted API wrappers that allow AI agents to safely interact with Grab’s core Risk Engine without risking platform stability.
• Optimize System Reliability: Own the technical stability, latency optimization (e.g., resolving deep asyncio blocking bugs), and token-cost efficiency of our internal agent platforms so our Data Analysts can focus purely on the Risk domain
• Collaborate as a Systems Fusion Expert: Partner closely with domain-expert Data Analysts to translate their tacit Trust & Safety knowledge into structured, machine-readable formats and scalable agentic skills.

Qualifications

What Essential Skills You Will Need

• At least 6 years of experience in data-heavy systems, backend engineering, applied machine learning, or a related field.
• A Bachelor's Degree (or higher) in Computer Science, Data Engineering, Analytics, Software Engineering, or related quantitative fields.
• Deep technical proficiency in Python, backend system architecture, and building secure APIs.
• Strong foundational data engineering expertise (you know how data structures, pipelines, ETL, vectorization, and embeddings work natively).
• Hands-on experience deploying and orchestrating LLMs using modern frameworks like LangSmith and LangGraph.
• Experience building and transitioning legacy, pre-LLM systems into modern AI infrastructures is highly preferred (so you understand the underlying architectural shift).
• Fluency in domain context (Trust & Safety / Fraud) is a strong plus, but an extreme openness to learning the domain and experimenting with new tech is an absolute requirement

Additional Information

Life at Grab

We care about your well-being at Grab, here are some of the global benefits we offer:

• We have your back with Term Life Insurance and comprehensive Medical Insurance.
• With GrabFlex, create a benefits package that suits your needs and aspirations.
• Celebrate moments that matter in life with loved ones through Parental and Birthday leave, and give back to your communities through Love-all-Serve-all (LASA) volunteering leave
• We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges.
• Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours

What We Stand For at Grab

We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.