Senior Full Stack Agentic AI Engineer — Layla AI

Expedia•Seattle, WA

About The Position

Layla AI, an AI-native travel-planning and booking company within Expedia Group’s Product organization, is building conversational experiences that help travelers move from inspiration to complete, bookable itineraries across flights, hotels, activities, and day-by-day plans. The Senior Full Stack Agentic AI Engineer will help build proactive travel-planning experiences, working across agent orchestration, backend services, data and retrieval systems, and user-facing product experiences. This is a high-autonomy, broad-scope senior individual-contributor role with shared ownership and close collaboration across Product, Design, Data, and Engineering. The engineer will turn emerging agentic capabilities into reliable, understandable, and useful traveler workflows, taking product opportunities from problem framing and exploration through implementation, launch, measurement, and iteration. The role calls for practical judgment, initiative, learning, and the ability to work in ambiguity while delivering production systems at global travel scale. Engineers are expected to use data, experimentation, and AI-assisted development practices to improve traveler experiences while remaining accountable for the code and systems they ship.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent professional experience.
At least 6 years of professional software engineering experience, including delivering production systems end to end.
Demonstrated experience building and operating full-stack applications, with meaningful depth in backend services and modern web or product-facing interfaces.
Professional experience integrating LLMs or AI/ML capabilities into production products, workflows, or platforms.
Experience with agentic patterns such as tool-using agents, multi-step workflows, orchestration, RAG, function calling, structured outputs, or human-in-the-loop systems.
Strong programming ability in at least one backend language such as Python, Java, Kotlin, Go, or TypeScript, plus experience contributing to a modern frontend stack.
Experience designing APIs, services, data models, integrations, automated tests, and production monitoring.
Ability to communicate technical tradeoffs clearly and collaborate effectively with Product, Design, Data, and Engineering partners.

Nice To Haves

Experience building AI agents or assistants that take actions through tools, APIs, or business workflows rather than only generating conversational responses.
Experience with agent orchestration or evaluation frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, OpenAI or Anthropic tooling, or comparable systems.
Experience with retrieval and knowledge systems, including vector search, hybrid search, embeddings, reranking, grounding, and data-quality controls.
Experience with streaming or real-time AI interactions, conversational interfaces, or multi-step user experiences.
Experience with React, TypeScript, Next.js, GraphQL, or comparable frontend technologies.
Experience with Python, FastAPI, Java/Kotlin services, REST or GraphQL APIs, event-driven architecture, and cloud-native systems.
Experience designing agent evaluation frameworks, offline test sets, online monitoring, quality rubrics, or experimentation programs.
Experience building systems with strong safety, privacy, security, reliability, and responsible-AI considerations.
Experience in travel, commerce, marketplace, recommendations, search, personalization, or other high-scale consumer products.
Experience using AI coding assistants or software-engineering agents in day-to-day development, including validating, correcting, and hardening generated output.
Experience working in a startup, incubation, or rapidly evolving product environment where priorities and requirements change quickly.

Responsibilities

Design, build, and operate agentic AI experiences for travel discovery, itinerary planning, personalization, and booking-related workflows.
Develop reliable agent orchestration patterns, including planning, tool use, routing, state management, memory, retries, guardrails, and human confirmation where appropriate.
Build and evolve backend services, APIs, data models, integrations, and event-driven workflows connecting agent experiences to Expedia Group and Layla capabilities.
Build user-facing experiences that make AI behavior clear, useful, and actionable across web and other relevant product surfaces.
Integrate retrieval, knowledge, and recommendation capabilities using suitable data contracts, embeddings, search, ranking, and grounding strategies.
Establish evaluation and observability practices for agentic systems, covering quality, task completion, latency, cost, safety, failure modes, and user impact.
Take shared ownership of a product surface from problem framing and solution exploration through implementation, launch, measurement, and iteration.
Translate ambiguous product opportunities into thin, testable increments and ship quickly while maintaining operational and architectural quality.
Partner with Product, Design, Data, and Engineering to shape product direction, define success metrics, and connect technical decisions to traveler outcomes.
Apply software engineering practices across testing, CI/CD, observability, security, incident response, and production support.
Use AI-assisted engineering tools to explore designs, accelerate implementation, improve test coverage, and investigate production behavior while retaining accountability for shipped code and systems.
Mentor teammates and raise quality through design and code reviews, documentation, reusable patterns, and hands-on technical leadership.
Improve reliability and observability of multi-step AI workflows and establish reusable patterns for agent tools, contracts, evaluation, guardrails, and production operations.
Use traveler feedback and experimentation results with Product and Design partners to improve AI experiences.
Contribute hypotheses, product ideas, and technical possibilities; test them thoughtfully with users, data, and teammates.
Make progress amid ambiguity while communicating risks, assumptions, and tradeoffs; make AI behavior understandable, measurable, recoverable, and appropriately constrained.
Share context, document decisions, and create patterns that help the team work effectively.