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Principal Software Development Engineer - Gen AI

Expedia Group

Principal Software Development Engineer - Gen AI

Expedia Group

India - Bangalore

·

On-site

·

Full-time

·

1w ago

Benefits & Perks

Healthcare

Parental Leave

Flexible Hours

Learning Budget

Remote Work

Healthcare

Parental Leave

Flexible Hours

Learning

Remote Work

Required Skills

Python

Distributed Systems

Platform Architecture

LLM Systems

AI/ML

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 workflows. 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 workflows, and ML. This is a principal-level role: you will set technical direction, define “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‑efficient 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/config 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.

  • Workflow Orchestration & Agentic Systems: Design multi‑step and multi‑agent workflows for complex tasks (triage/routing, map‑reduce analysis, reflection/verification loops), using workflow/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 flows where required.

  • ML Collaboration & Model Integration: Collaborate with data scientists and ML engineers when problems require specialized

  • ML models (forecasting, anomaly detection, classification, ranking). You will help turn models into reliable services and integrate them into workflows.

  • Evaluation, Testing & Red Teaming: Define offline 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 classification, audit trails, and strong controls around tool execution. Ensure solutions meet enterprise standards for governance, privacy, and responsible AI.

  • Metrics, Measurement & Business Impact: Define 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— defining scope, success metrics, milestones, and operational readiness.

Experience and Qualifications:

Minimum Qualifications:

  • Bachelor’s or Master’s in Computer Science, Engineering, Mathematics, or a related field; 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)​

Preferred Qualifications:

  • Experience leading high-performing engineering teams and cross-team technical initiatives.

  • Proficiency in applying AI to practical technology solutions, including ML/deep learning experience (e.g., Tensor Flow) and strong Python skills.

  • Experience in backend development using Java and Kotlin.

  • Experience establishing standards for operational excellence, reliability, and code quality at a multi-project level.

  • Proven ability to mentor other engineers and guide technology choices across teams.

Accommodation requests

If you need assistance with any part of the application or recruiting process due to a disability, or other physical or mental health conditions, please reach out to our Recruiting Accommodations Team through the Accommodation Request.

We are proud to be named as a Best Place to Work on Glassdoor in 2024 and be recognized for award-winning culture by organizations like Forbes, TIME, Disability:IN, and others.

Expedia Group's family of brands includes: Brand Expedia®, Hotels.com®, Expedia® Partner Solutions, Vrbo®, trivago®, Orbitz®, Travelocity®, Hotwire®, Wotif®, ebookers®, Cheap Tickets®, Expedia Group™ Media Solutions, Expedia Local Expert®, Car Rentals.com™, and Expedia Cruises™. © 2024 Expedia, Inc. All rights reserved. Trademarks and logos are the property of their respective owners. CST: 2029030-50

Employment opportunities and job offers at Expedia Group will always come from Expedia Group’s Talent Acquisition and hiring teams. Never provide sensitive, personal information to someone unless you’re confident who the recipient is. Expedia Group does not extend job offers via email or any other messaging tools to individuals with whom we have not made prior contact. Our email domain is @expediagroup.com. The official website to find and apply for job openings at Expedia Group is careers.expediagroup.com/jobs.

Expedia is committed to creating an inclusive work environment with a diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, gender, sexual orientation, national origin, disability or age.

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About Expedia Group

Expedia Group

An online travel agency that provides hotel reservations, airline tickets, and vacation packages.

10,001+

Employees

Seattle

Headquarters

$6.8B

Valuation

Reviews

3.8

9 reviews

Work Life Balance

4.2

Compensation

3.5

Culture

4.1

Career

4.0

Management

3.4

75%

Recommend to a Friend

Pros

Supportive work environment and colleagues

Good work-life balance

Great benefits and perks

Cons

Poor management and leadership issues

Compensation below market rate

Organizational chaos from acquisitions

Interview Experience

7 interviews

Difficulty

3.0

/ 5

Duration

14-28 weeks

Interview Process

1

Final Round

2

Coding Assessment

3

Tech Round

4

HackerRank Round

5

Digital Interview

6

Final Interview

Common Questions

Technical coding problems

System design

Behavioral questions

Algorithm implementation