Job Description
As a Software Development Engineer III, you will design, build, and operate backend platform services that power real-time fraud detection, automated remediation, and investigation workflows across the Expedia marketplace.
This is a hands-on engineering role focused on building reliable distributed systems while integrating modern AI capabilities into fraud detection and operational workflows. The ideal candidate combines strong backend engineering fundamentals with experience building and deploying AI-powered systems in production environments.
In this role you will:
- Design, develop, deploy, and maintain backend services with clear latency, reliability, and reliability requirements.
- Build scalable distributed systems supporting real-time fraud detection, investigation, and automated remediation workflows.
- Develop and productionize ML-powered components with Data Science teams, including feature pipelines, online inference, experimentation, and monitoring.
- Apply Generative AI techniques (e.g., LLM-based reasoning, evidence summarization, automated investigation workflows) to improve detection speed and operational efficiency.
- Own delivery of projects end-to-end, including design, implementation, testing, deployment, and operational support.
- Modernize platform architecture using microservices, streaming/data pipelines, CI/CD automation, and configuration-driven controls.
- Ensure AI-enabled systems are observable, testable, explainable, and safe with appropriate guardrails and fallback mechanisms.
- Mentor junior engineers through design discussions, code reviews, and technical guidance.
- Collaborate with engineering, product, operations, and security teams to deliver measurable improvements in fraud prevention.
Minimum Qualifications
- 5+ years of software engineering experience with a Bachelor’s degree, or 3+ years with a Master’s degree in Computer Science or a related field.
- Strong experience building and operating distributed backend systems in production environments.
- Proficiency in one or more backend languages such as Java, Python, Scala, or Go, with experience building APIs and microservices.
- Experience developing cloud-native applications on AWS, GCP, or Azure.
- Experience working with data systems such as SQL/NoSQL databases, streaming platforms (e.g., Kafka), or data platforms.
- Hands-on experience integrating ML or AI capabilities into production systems, such as model inference services, feature pipelines, or decisioning workflows.
- Experience deploying AI/ML systems in production where model outputs influence product behavior, automated decisions, or operational workflows.
- Strong understanding of software engineering fundamentals including system design, testing strategies, and maintainable architecture.
Preferred Qualifications
- Experience building LLM-powered applications or Generative AI systems in production.
- Hands-on experience with RAG architectures, vector databases, and retrieval pipelines.
- Experience building agentic workflows or AI orchestration systems using frameworks such as LangChain, LangGraph, Model Context Protocol (MCP), or similar frameworks.
- Experience designing end-to-end AI application architectures, including retrieval pipelines, model orchestration, evaluation frameworks, and production monitoring.
- Experience working on real-time decisioning systems, such as fraud detection, risk scoring, or recommendation platforms.
- Experience mentoring engineers and contributing to system design discussions.
