Senior Software Development Engineer - GenAI Platform

Expedia Group•Chicago, IL

1d•$171,500 - $240,000

About The Position

The Senior Software Development Engineer is part of Expedia Group’s GenAI Platform team, which builds and operates shared generative AI tooling, services, and guardrails. The role leads the design, delivery, and evolution of scalable GenAI services and multi-agent infrastructure that enable product and machine-learning teams to safely experiment with, deploy, and scale GenAI-powered experiences across Expedia Group. The position focuses on secure, observable, and reliable GenAI capabilities, including RAG managers, data planes, vector-store services, and platform guardrails. The engineer partners with product and domain engineering teams to shape platform architecture, document and promote adoption practices, and help teams build and 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.
At least 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.
Proficiency in Python and experience developing production-grade web services with FastAPI or a similar modern framework, such as Flask, Django, or another asynchronous service framework.
Solid understanding of system design, API design, and data modeling.
Experience building and running backend APIs in public or hybrid cloud environments.
Hands-on exposure to core cloud services, preferably AWS, and at least one data-store technology, such as relational, NoSQL, or search.
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 with 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 such as LangChain, LlamaIndex, or LangGraph, and evaluation or observability frameworks such as LangSmith or Langfuse.
Experience safely integrating and operating AI/ML-enabled solutions.
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 guardrails such as authentication, quotas, and evaluation hooks.
Background building internal platforms, SDKs, or shared services adopted by multiple product teams, including documentation, paved-road patterns, and cross-functional collaboration.
Experience designing or implementing workflow- and agent-orchestration solutions, such as n8n or Flowise, for multi-tool or multi-agent GenAI applications.
Familiarity with AI-driven platform architectures and safety-by-design practices.

Responsibilities

Design, build, and operate Python/FastAPI backend services and APIs for GenAI Platform capabilities, including RAG managers, data planes, and vector-store services.
Apply strong system design, API design, and data-modeling practices.
Write clean, maintainable, well-tested code and participate in code reviews, CI/CD, automated testing, monitoring, alerting, and on-call rotations where applicable.
Integrate platform services with internal and external LLM providers, Elasticsearch-based vector stores, and Aurora PostgreSQL for scalable RAG and retrieval workflows.
Develop and evolve platform guardrails and safety features, including authentication, API-key management, workload-to-workload authentication, and evaluation and observability hooks.
Help ensure AI-enabled applications are secure, responsible, observable, cost-aware, and safely operated.
Collaborate with staff and principal engineers, product managers, applied scientists, and other engineering teams to refine requirements, shape platform architecture, and deliver production-ready improvements.
Contribute to platform architecture, including multi-tenant vector stores and workflow integrations.
Create and maintain design documents, runbooks, implementation guides, and how-to documentation to support adoption and extension of platform services.