Software Development Engineer III - AI Engineer

Expedia Expedia · Hospitality · Austin, TX

Software Development Engineer III role focused on designing and implementing real-time, AI-powered fraud and abuse defenses using Generative AI, Agentic AI, LLMs, and RAG. The role involves building and operating cloud-native decisioning and automated remediation systems, integrating AI into workflows, and simplifying the platform. Requires experience with ML/AI solutions in production, LLM/multi-agent applications, RAG architectures, and cloud-native engineering.

What you'd actually do

  1. 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.
  2. Build, deploy, and operate ML-powered capabilities in production, partnering closely with Data Science/ML Scientists on features, experimentation, and monitoring.
  3. Incorporate and integrate Generative and Agentic AI into fraud detection and remediation workflows to reduce time to detect and mitigate attacks.
  4. Help simplify and modernize the platform (streaming/data pipelines, microservices, CI/CD, configuration-driven controls) so that teams can iterate quickly and safely.
  5. Participate in evaluating vendors and in-house solutions for performance, cost, and risk posture, contributing data and implementation perspectives into the decision process.

Skills

Required

  • Bachelor’s degree in Computer Science or a related technical field; or equivalent related professional experience
  • 5+ years of software engineering
  • developing, deploying and operating ML/AI driven solutions in production
  • building, monitoring and debugging LLM and multi-agent applications
  • LangChain, LangGraph, Langfuse, or equivalent
  • RAG-based architecture
  • 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)
  • ship with Data Science/ML Science partners
  • modern programming language
  • system design (LLD)
  • API design
  • data modeling for AI-enabled services
  • distributed cloud-native engineering at scale (AWS, GCP, or Azure)
  • microservices
  • API-driven design
  • SQL/NoSQL databases
  • data streaming/processing (Kafka, Flink, Spark)

Nice to have

  • Proven experience building and oper

What the JD emphasized

  • significant experience developing, deploying and operating ML/AI driven solutions in production
  • Demonstrable experience building, monitoring and debugging LLM and multi-agent applications
  • RAG-based architecture experience

Other signals

  • AI-powered fraud and abuse defenses
  • ML-powered capabilities in production
  • Generative and Agentic AI into fraud detection and remediation workflows
  • LLM and multi-agent applications
  • RAG-based architecture