Introduction to the Team:    

Our Technology Team partners with teams across Expedia Group to create innovative products services and tools to deliver high-quality experiences for travelers partners and our employees. A singular technology platform powered by data and machine learning provides secure differentiated and personalized experiences that drive loyalty and traveler satisfaction.      

At Expedia Group Fraud & Risk (EFR) we are the guardians of marketplace trust. We protect EG from financial loss and brand damage securing every channel for our customers partners suppliers and employees. Our world class engineering and machine learning turn massive noisy signals into real time decisions that minimize risk while keeping traveler friction low. 

What makes this work unique: in travel risk doesn’t stop at checkout it spans the entire journey. The global ecosystem is vast and fragmented control points are scarce and every leg introduces new unknowns. Our mission is bold: deliver a fully trusted journey from purchase to safe return home. That’s hard and exactly why science and technology matter most. 

If you’re energized by complex high impact problems with no obvious answers and want to shape the future of risk with AI on a global scale join us. Let’s redefine how the world travels safely confidently and at speed. 

Lead the architecture and delivery of real-time AI-powered fraud and abuse defenses on a global scale. You will design cloud-native decisioning and automated remediation systems simplify our platform and align cross-functional teams to measurable outcomes. 

In this role you will:    

• Own end-to-end architecture for low-latency risk decisioning (signals + rules + models) and automated remediation 

• Build deploy and operate ML in production; partner closely with DS/ML on features experimentation and monitoring 

• Simplify and modernize the platform (streaming/data pipelines microservices CI/CD configuration-driven controls) to speed safe iteration 

• Drive build-vs-buy evaluations and benchmark vendors against in-house solutions for performance cost and risk posture 

• Establish clear SLOs observability and safety/rollback mechanisms; ensure security privacy and compliance are built in 

• Mentor senior engineers and align Product Ops Security and Compliance on roadmap and outcomes 

• Communicate clearly with executives and engineering: distill strategy make tradeoffs explicit and document decisions. 

Minimum Qualifications:    

• 10+ years of software engineering including 4+ years leading architecture for real-time high-scale systems 

• Proven experience building and operating fraud/risk systems in production (e.g. booking payments/chargebacks ATO content/supplier integrity) with measurable impact on losses false positives and manual effort 

• Production ML experience (supervised/anomaly detection feature pipelines online inference monitoring/retraining); ability to ship with DS/ML partners 

• Cloud-native engineering at scale (AWS GCP or Azure): event-driven services data/stream processing observability cost and reliability best practices 

• Strong coding skills in one or more of Java/Scala/Go/Python; microservices APIs and infrastructure-as-code 

• Security privacy and regulatory fundamentals for sensitive data and risk decisioning 

• Experience with executive communication: can brief executives translate strategy into actionable engineering plans and align teams 

Preferred Qualifications:    

• LLM/agentic techniques applied to fraud (e.g. investigation assist pattern discovery automated playbooks) and retrieval over enterprise data 

• Graph/sequence modeling or entity resolution at scale; device and behavioral signals 

• Track record reducing manual operations via automation and platform simplification 

• Hands-on with experimentation platforms and real-time rules/model simulation/back testing 

• Deep cloud cost/performance tuning and capacity planning 

• Marketplace travel or e-commerce experience 

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