Software Development Engineer III

Expedia•Seattle, WA

87d•Hybrid

About The Position

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. 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. This role is for an AI Engineer who will design and deliver real-time, AI-powered fraud and abuse defenses on a global scale. You will build and operate cloud-native decisioning and automated remediation systems, help simplify our platform, and collaborate with cross-functional teams to achieve measurable outcomes. You will incorporate and integrate Generative and Agentic AI into fraud detection and remediation workflows to reduce time to detect and mitigate attacks. You will also help simplify and modernize the platform (streaming/data pipelines, microservices, CI/CD, configuration-driven controls) so that teams can iterate quickly and safely. Additionally, you will participate in evaluating vendors and in-house solutions for performance, cost, and risk posture, contributing data and implementation perspectives into the decision process. You will implement and maintain SLOs, observability, and safety/rollback mechanisms for the services you own, ensuring security, privacy, and compliance requirements are met by design. Finally, you will collaborate closely with engineering, product, operations, security, and compliance teams to deliver outcomes, sharing knowledge with peers and supporting a high bar for engineering excellence, and mentor other engineers and champion the use of AI within the organization.

Requirements

Bachelor’s degree in Computer Science or a related technical field; or equivalent related professional experience.
5+ years of software engineering, including significant experience developing, deploying and operating ML/AI driven solutions in production.
Demonstrable experience building, monitoring and debugging LLM and multi-agent applications, with frameworks and platforms such as LangChain, LangGraph, Langfuse, or equivalent.
RAG-based architecture experience, including data orchestration frameworks such as LlamaIndex, vector databases such as Pinecone, or equivalent.
Exposure to various LLM providers such as OpenAI, Gemini, and Anthropic.
Production ML experience (supervised/anomaly detection, feature pipelines, online inference, monitoring/retraining); ability to ship with Data Science/ML Science partners.
Hands-on proficiency in at least one modern programming language and core software engineering practices, including system design (LLD), API design, and data modeling for AI-enabled services.
Familiarity with distributed cloud-native engineering at scale (AWS, GCP, or Azure), microservices, API-driven design, SQL/NoSQL databases and data streaming/processing (Kafka, Flink, Spark).

Nice To Haves

Proven experience building and operating either fraud and risk systems in production, or other similarly ML/AI heavy systems.
Graph/sequence modeling or entity resolution at scale; device and behavioral signals.
Track record reducing manual operations via automation and platform simplification.
Experience with cloud cost/performance tuning and capacity planning.
Marketplace, travel, or e-commerce experience.

Responsibilities

Contribute to the design and implementation of low-latency risk decisioning (signals + rules + models) and automated remediation, owning the quality and reliability of the services and components you build.
Build, deploy, and operate ML-powered capabilities in production, partnering closely with Data Science/ML Scientists on features, experimentation, and monitoring.
Incorporate and integrate Generative and Agentic AI into fraud detection and remediation workflows to reduce time to detect and mitigate attacks.
Help simplify and modernize the platform (streaming/data pipelines, microservices, CI/CD, configuration-driven controls) so that teams can iterate quickly and safely.
Participate in evaluating vendors and in-house solutions for performance, cost, and risk posture, contributing data and implementation perspectives into the decision process.
Implement and maintain SLOs, observability, and safety/rollback mechanisms for the services you own, ensuring security, privacy, and compliance requirements are met by design.
Collaborate closely with engineering, product, operations, security, and compliance teams to deliver outcomes, sharing knowledge with peers and supporting a high bar for engineering excellence.
Mentor other engineers and champion the use of AI within the organization.