Principal Software Development Engineer - Gen AI

Requirements

• Bachelor's or Master's in Computer Science, Engineering, Mathematics, or a related fi eld; or equivalent related professional experience.
• 12+ years of experience developing production-quality code in a professional software engineering role.
• 3+ years of experience focused on AI/ML and/or building production LLM systems.
• Strong background in distributed systems and platform architecture (e.g., Kubernetes, AWS, microservices)
• Prefe

Benefits

Additional Information

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. Why Join Us? To shape the future of travel, people must come first. Guided by our Values and Leadership Agreements, we foster an open culture where everyone belongs, differences are celebrated and know that when one of us wins, we all win. We provide a full benefits package, including exciting travel perks, generous time-off, parental leave, a flexible work model (with some pretty cool offices), and career development resources, all to fuel our employees' passion for travel and ensure a rewarding career journey. We're building a more open world. Join us. Introduction to the Team At Expedia Group, our Finance Engineering team is reimagining the systems and data backbone that power corporate and business-critical accounting functions. We are hiring a Principal GenAI Engineer to build production-grade AI systems that accelerate real business work fl ows . You will work across teams to understand domain problems, identify high‑impact opportunities for AI, and deliver scalable, secure, and observable systems using LLMs, retrieval (RAG), agentic work fl ows, and ML . This is a principal-level role: you will set technical direction, de fi ne "golden paths," and raise engineering standards for how GenAI is built and operated across the organisation. In this role, you will: Platform Architecture & Scalability : Design and implement cloud‑native, cost‑e ffi cient GenAI architectures (services, APIs, data paths, and infrastructure) that are production-ready, observable, and resilient. GenAI Platform Enablement : Create shared capabilities such as model routing/gateways, prompt/con fi g management, retrieval services, evaluation harnesses, and libraries so multiple product teams can ship consistently. Production Performance & Resilience : Tackle deployment realities including latency/throughput optimization, caching strategies, rate limiting, multi‑tenant isolation, failure handling, and "safe fallbacks" when models or dependencies degrade. Reliability, Guardrails & Trust : Implement techniques to reduce hallucinations and variability via grounding, structured outputs, tool use, and robust guardrails. Ensure systems are testable, measurable, and maintainable over time. Retrieval & Knowledge Systems (RAG) : Build ingestion and retrieval pipelines (chunking, embeddings, metadata, hybrid retrieval, reranking) so LLMs can answer with evidence/citations and predictable quality. Work fl ow Orchestration & Agentic Systems : Design multi‑step and multi‑agent work fl ows for complex tasks (triage/routing, map‑reduce analysis, re fl ection/veri fi cation loops), using work fl ow/orchestration frameworks where helpful (e.g., n8n, Temporal, AWS Step Functions), including state management and error recovery. Tooling, Integrations & Controls : Implement a secure tool registry and integrations so agents can call deterministic tools (SQL/querying, calculators, internal APIs, automation actions) with appropriate constraints and human approval fl ows where required. ML Collaboration & Model Integration : Collaborate with data scientists and ML engineers when problems require specialized ML models (forecasting, anomaly detection, classi fi cation, ranking). You will help turn models into reliable services and integrate them into work fl ows. Evaluation, Testing & Red Teaming : De fi ne o ffl ine and online evaluation strategies (golden datasets, regression testing, LLM- as-judge where appropriate) and run safety/robustness testing before release. Governance, Security & Access Control : Design autonomous systems with security by default-agent identity, least‑privilege access, secret handling, data classi fi cation, audit trails, and strong controls around tool execution. Ensure solutions meet enterprise standards for governance, privacy, and responsible AI. Metrics, Measurement & Business Impact : De fi ne success metrics up front and instrument systems end‑to‑end (quality, latency, cost, adoption). Use data to prioritize improvements and communicate impact to stakeholders. Prototype-to-Production Execution : Lead proof‑of‑concepts and drive the transition into hardened, supported products- de fining scope, success metrics, milestones, and operational readiness. Experience and Qualifications:

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