Senior Software Development Engineer - Agentic AI Experience

Expedia Expedia · Hospitality · Austin, TX

Senior Software Development Engineer role focused on building deeply integrated, AI-powered experiences that transform traveler and partner interactions into proactive, autonomous, multimodal journeys. The role involves leading system design, partnering with stakeholders, championing AI/ML adoption, driving operational excellence, and mentoring engineers. Requires experience in building and operating production services, leading AI/ML integration, and preferably experience with agentic AI applications and orchestration frameworks.

What you'd actually do

  1. Lead team-level design and development across the domain, driving high-level system design (HLD) for complex cross-service dependencies, establishing data architecture and measurable outcomes, and delivering technical solutions across a multi-year horizon.
  2. Partner closely with Product, Engineering, and other technical stakeholders to frame and solve complex business and technical challenges. Leverage data-driven decision-making, incorporate long-term data strategy into solution design, and drive initiatives from concept through measurable outcomes.
  3. Champion the adoption and integration of AI/ML capabilities across critical systems. Design and implement continuous learning and feedback mechanisms, including human-in-the-loop workflows, automated evaluation pipelines, and retraining strategies, while collaborating with MLS and Product teams to maximize customer and business impact.
  4. Drive operational excellence at domain scale, owning operational backlogs, establishing regular domain reviews, and tracking reliability and improvement metrics across the services you lead. Lead incident response and major production events.
  5. Influence broadly within the domain, collaborating with peers, Principal Engineers, and product stakeholders to align technical direction and priorities. Play a key role in producing the domain technical vision and establishing inner-sourcing engagements with other teams.

Skills

Required

  • 8+ years (Bachelor's) or 6+ years (Master's) of professional software development experience building and operating production services or applications at scale.
  • Demonstrated ability to lead architecture and high-level design for complex, multi-service platforms, managing cross-system dependencies, defining resilient integration patterns, performing failure-mode analysis, and establishing clear service and data contracts.
  • Proficiency in at least one modern programming language (such as Java, Kotlin, C#, Python, or similar) and proven ability to set coding standards and own quality definition, measurement, and review cadence across a team or domain.
  • Demonstrated experience leading AI/ML adoption and integration across complex production systems, defining measurement and evaluation strategies, proactively managing model and operational risks, and designing scalable feedback mechanisms—including human-in-the-loop and automated retraining approaches—to drive continuous improvement and business impact.

Nice to have

  • Experience building agentic AI applications and agents from the ground up, with strong hands-on depth in frameworks such as LangGraph or similar orchestration patterns for agent development.
  • Demonstrated expertise in prompt engineering, understanding LLM behavior, and making practical model-selection tradeoff decisions across available providers and model types based on product needs.
  • Experience designing and evaluating agentic architecture patterns, including multi-agent and skill-based architectures, and determining which approach best fits a given use case or customer problem.
  • Hands-on experience building or integrating AI/ML‑enabled solutions in production (such as retrieval-augmented generation, intelligent agents, recommendation or decision systems), with strong focus on safety, guardrails, monitoring, and continuous improvement.
  • Demonstrated expertise in architecting and scaling AI-centric services using modern engineering practices, including prompt and policy management, evaluation and observability frameworks, agent orchestration, and governance controls.

What the JD emphasized

  • AI/ML capabilities
  • automated evaluation pipelines
  • retraining strategies
  • AI/ML adoption and integration
  • human-in-the-loop workflows
  • agentic AI applications
  • agent orchestration

Other signals

  • AI-powered experiences
  • autonomous, multimodal journeys
  • AI/ML capabilities
  • agentic AI applications
  • agent orchestration