Senior Software Development Engineer - GenAI Platform

Expedia•Chicago, IL

46d•Hybrid

About The Position

Expedia Group brands power global travel for everyone, everywhere. We design cutting-edge tech to make travel smoother and more memorable, and we create groundbreaking solutions for our partners. Our diverse, vibrant, and welcoming community is essential in driving our success. The Senior Software Developer role is part of the GenAI Platform team. The GenAI Platform team builds and operates Expedia Group’s shared generative AI platform - tooling, services, and guardrails - that enable product and ML teams to safely experiment with, deploy, and scale GenAI-powered experiences across our brands. You will lead the design, delivery, and evolution of scalable GenAI services and multi-agent infrastructure that power secure, observable GenAI capabilities across Expedia Group. By shaping multi-agent platform architecture, codifying guardrails and best practices, and partnering closely with product and domain engineering teams, you will accelerate how internal teams build, integrate, and safely operate AI-enabled experiences for travelers and partners at global scale.

Requirements

Bachelor’s degree in Computer Science or a related technical field; or Equivalent related professional experience.
8+ years of relevant professional experience, including building and operating backend services and APIs with end-to-end ownership from design through deployment and ongoing operational support.
Proven proficiency in Python and experience developing production-grade web services using FastAPI or similar modern frameworks (such as Flask, Django, or other async service frameworks), with solid understanding of system design, API design, and data modeling.
Experience building and running backend APIs in public or hybrid cloud environments, with hands-on exposure to core cloud services (AWS preferred) and at least one data store technology (relational, NoSQL, or search), such as Aurora PostgreSQL or Elasticsearch.
Familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products, including exposure to LLM-powered capabilities, embeddings, or RAG patterns, or demonstrated ability to ramp up quickly using internal GenAI Platform tools and documentation.

Nice To Haves

Deep experience building and operating Python-based backend APIs at scale on AWS or similar cloud providers, including performance tuning, resilience patterns, and advanced API and data modeling for high-traffic or multi-tenant services.
Hands-on experience with RAG and vector store tooling (for example, LangChain, LlamaIndex, LangGraph) and evaluation or observability frameworks (such as LangSmith or Langfuse), including safely integrating and operating AI/ML‑enabled solutions that improve outcomes.
Experience integrating and optimizing LLM-powered systems end to end, including prompt and retrieval design, embeddings management, Elasticsearch vector search, Aurora PostgreSQL schemas, and platform-level guardrails such as auth, quota, and evaluation hooks.
Background building internal platforms, SDKs, or shared services adopted by multiple product teams, including clear documentation, paved road patterns, and close collaboration with cross-functional partners (product management, applied science, and engineering).
Experience designing or implementing workflow and agent orchestration–based solutions (for example, using n8n, Flowise, or similar) to build multi-tool or multi-agent GenAI applications, and familiarity with AI-driven platform architectures and safety-by-design practices.

Responsibilities

Design, build, and operate Python/FastAPI backend services and APIs that power GenAI Platform capabilities such as RAG managers, data planes, and vector store services, with strong focus on system design, API design, and data modeling.
Implement clean, maintainable, and well-tested code using modern engineering practices including code reviews, CI/CD pipelines, automated testing, monitoring, alerting, and participating in on-call rotations where applicable.
Integrate platform services with internal and external LLM providers, Elasticsearch-based vector stores, and Aurora PostgreSQL as part of scalable, reliable RAG and retrieval workflows across multiple domains.
Contribute to and evolve GenAI platform guardrails and safety features (authentication, API key management, workload-to-workload authentication, evaluation and observability hooks) so AI-enabled applications are secure, responsible, observable, cost-aware, and safely integrate and operate AI/ML‑enabled solutions that improve outcomes.
Partner closely with staff and principal engineers, product managers, applied scientists, and other engineering teams to refine requirements, shape platform architecture (including multi-tenant vector stores and workflow integrations), and deliver incremental, production-ready value.
Create and maintain clear technical documentation (design docs, runbooks, implementation guides, and how-tos) that enables other teams to successfully adopt, extend, and build on GenAI Platform services and paved roads.