User Experience Engineer III - Full Stack (web Heavy - React.js + Java/kotlin/node.js)

Expedia Expedia · Hospitality · Gurgaon, India

Full stack engineer with a focus on web technologies (React.js, Java/Kotlin/Node.js) for Expedia's flights shopping platform. The role involves designing, building, and maintaining web experiences, APIs, and data models, with a requirement to safely integrate and operate AI/ML-enabled solutions to improve outcomes. Experience with AI-driven tools and applying AI/ML concepts to real-world products is necessary.

What you'd actually do

  1. Design, build, and maintain full stack web experiences using React.js on the front end and Java/Kotlin services on the back end, ensuring performant, resilient, and secure user flows.
  2. Implement clean, reusable UI components and client-side state management patterns, collaborating with designers to translate experience requirements into responsive, accessible interfaces.
  3. Design low-level system components, RESTful APIs, and data models that support user-centric functionality, ensuring clear contracts, versioning, and maintainability across services.
  4. Own the end-to-end lifecycle of features from technical design and implementation through testing, deployment, monitoring, and iterative improvement based on data and customer feedback.
  5. Safely integrate and operate AI/ML‑enabled solutions that improve outcomes, including familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products.

Skills

Required

  • React.js
  • Java/Kotlin services
  • RESTful APIs
  • low-level system components
  • data models
  • end-to-end feature ownership
  • automated testing
  • deployment
  • monitoring
  • incident response
  • AI-driven tools or systems familiarity

Nice to have

  • Node.js
  • front-end architectures at scale
  • performance optimization
  • code-splitting
  • accessibility
  • client-side state management
  • backend engineering depth
  • service decomposition
  • distributed systems integration
  • caches
  • data stores
  • operational excellence
  • observability
  • alerting
  • automated quality gates
  • data-driven decision making
  • personalization
  • recommendations
  • intelligent assistance
  • modern full stack delivery practices
  • CI/CD
  • feature flags
  • experimentation
  • canary releases
  • AI-assisted development
  • experimentation workflows

What the JD emphasized

  • safely integrate and operate AI/ML‑enabled solutions
  • familiarity with AI-driven systems, tools, or workflows
  • applying AI/ML concepts to real world products
  • Practical familiarity with AI-driven tools or systems within the software development lifecycle
  • ability to safely integrate and work with AI/ML‑enabled capabilities in products or workflows