AI Engineer III

Expedia Group•Austin, TX

4d•$146,000 - $204,500•Onsite

About The Position

Expedia Group is a global travel company that connects travelers, partners, and advertisers through its consumer brands, B2B network, and travel advertising business. The Customer Data Platform (CDP) at Expedia Group focuses on building innovative products, services, and tools to deliver high-quality data experiences for travelers, partners, and internal teams. This platform is powered by data and machine learning to provide secure, differentiated, and personalized experiences that drive loyalty and customer satisfaction. As an AI Engineer III, you will lead projects, design, and build scalable, reliable data services and pipelines using AI tools and technologies to support analytics, reporting, and data-driven product experiences. This role requires a combination of strong software engineering fundamentals, data engineering skills, and AI expertise to deliver secure, high-quality data products on modern cloud infrastructure.

Requirements

Bachelor’s degree in computer science or a related technical field; or Equivalent related professional experience
5+ years of relevant professional experience
Experience with owning data products, with demonstrated impact within a domain or product
Proven experience delivering at least one significant end to end service or product within a product or domain using AI tools (like Claude, Codex, copilot).
Proficiency in at least one modern programming language (like Java, Scala) and strong computer science fundamentals (data structures, algorithms, object-oriented or functional design), with hands-on experience in system design (LLD), API design, and data modeling.
Experience working with data pipelines, batch and stream, including building, consuming, and evolving them.

Nice To Haves

Experience designing and building reliable, scalable systems on cloud platforms, including contributing to service boundaries, resilience mechanisms, and operational best practices.
Strong proficiency using AI tools and techniques as part of everyday software engineering work (for example, exploring implementation options, generating and hardening code, enhancing test coverage, or analyzing production behavior), with sound judgment about when human-only review is required.
Experience developing frameworks for AI-driven coding agents, including MCP and spec-driven development methodologies.
Experience defining and enforcing AI governance practices to ensure responsible, secure, and compliant AI system development.
Experience with big data technologies like Spark, Kafka, Flink and no-SQL data stores like Cassandra, Scylla and Iceberg.
Demonstrated ability to collaborate and influence across disciplines — partnering with engineering, product, and data stakeholders to move designs forward, unblock dependencies, and land changes that span multiple teams.
Track record of leading technical workstreams that simplify components or services, improve performance or reliability, or increase developer productivity within and adjacent to your team.

Responsibilities

Own and evolve complex, business-critical data products end to end — from technical design and implementation through deployment, monitoring, and day-to-day production support, escalating appropriately on the most complex or cross-org decisions.
Apply modern engineering practices and use AI as a co-pilot across the software development lifecycle to clarify requirements, explore designs, accelerate coding, strengthen automated testing, and improve operational excellence and reliability.
Collaborate with partners across engineering, product, data, and business functions — contributing to cross-team designs, surfacing trade-offs clearly, and helping align simple, resilient solutions.
Design and integrate services, APIs, and data models so that high-quality data and capabilities are easily consumable by both human users and AI systems, working within established architectural patterns and guardrails.
Share knowledge and mentor others, especially earlier-career engineers, through code reviews, pairing, and design discussions — including helping teammates use AI tools effectively and responsibly as part of their daily workflow.