Principal AI Forward Deployed Engineer

Expedia Group•San Jose, CA

7d•$231,000 - $323,500

About The Position

Within Expedia Group’s CTO Enablement organization, the AI Enablement and Culture Office helps the company become AI-native by turning experimentation into measurable business impact, improving capacity, speed, and quality, and building enterprise AI fluency. The Principal AI Forward Deployed Engineer embeds with business units and enterprise functions to identify valuable AI opportunities, translate ambiguous business challenges into measurable outcomes, and rapidly prototype, productionize, and scale AI-powered solutions. This is a highly influential technical leadership role that combines hands-on engineering with executive partnership, coaching, and change leadership. The engineer guides teams in adopting AI responsibly and at scale, develops reusable patterns and tools, and keeps meaningful outcomes for travelers, partners, and employees at the center. The role owns AI initiatives from discovery through deployment, adoption, value measurement, and continuous improvement, while shaping domain-level technology vision and engineering practices across teams.

Requirements

Bachelor’s degree and 12 or more years of professional software engineering experience, or a master’s degree and 10 or more years of professional software engineering experience, or equivalent professional experience.
Demonstrated ownership of complex systems, workflows, or platforms supporting a business unit or function and integrating with broader Expedia Group systems and cross-domain capabilities.
Deep expertise in professional software engineering practices across the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, operations, system design, API design, data modeling, and observability.
Experience making technology and architecture choices across teams and multiple technologies or languages, while guiding others toward well-defined solutions for complex systems.
Hands-on experience building, deploying, or operating AI-enabled systems, tools, agents, or workflows, and applying AI/ML concepts to real-world products or business processes.
Experience working directly with partner business units and their teams to understand ambiguous needs, deliver usable technical solutions quickly, and drive adoption beyond an initial prototype or pilot.

Nice To Haves

Experience defining AI engineering, software engineering, or architecture best practices at a department or organizational level and establishing standards for operational excellence, code quality, scalability, resilience, observability, testability, evaluation, and responsible AI across multiple projects.
Ability to shape product and technology direction, develop business-unit and domain-level AI architecture vision, and create reusable patterns that can scale across Expedia Group.
Experience designing and productionizing LLM, generative AI, retrieval-augmented generation, agentic, conversational, or AI-assisted workflow solutions.
Experience establishing AI evaluation and governance practices, including quality measurement, hallucination mitigation, confidence scoring, human oversight, security, privacy, auditability, and monitoring.
Experience serving as a trusted technical partner to executive and cross-organizational leaders, influencing domain strategy and enabling decisions across cross-functional teams.
Demonstrated success mentoring engineers across multiple levels, coaching senior individual contributors in leadership skills, identifying rising talent, and supporting engineering-management priorities through technical partnership when needed.
Experience evaluating industry trends pragmatically, distinguishing durable opportunities from disruptive or short-lived technologies, and applying AI/ML-enabled approaches selectively to improve engineering, product, or operational outcomes.

Responsibilities

Partner with business-unit and functional leaders, domain experts, end users, and technologists to discover high-value AI opportunities, define measurable outcomes, and establish delivery plans.
Work directly with business units and their teams to understand workflows, constraints, data, and systems, and own engagements through delivery, adoption, and value realization.
Own the full lifecycle of AI initiatives, from discovery and use-case framing through technical design, prototyping, production deployment, adoption, measurement, and continuous improvement.
Translate ambiguous business problems into measurable outcomes, explicit assumptions, data requirements, and scalable, resilient, maintainable AI-enabled systems.
Define and communicate a north-star technology vision for AI capabilities within a business-unit domain, workflow, or capability area, balancing speed to value with reliability, security, privacy, cost, and maintainability.
Design complex systems operating across a business unit, applying system design, API design, data modeling, observability, evaluation, and testability practices across technologies and languages.
Evaluate and select models, platforms, tools, and architectural patterns for AI use cases, including retrieval, orchestration, agentic workflows, human-in-the-loop controls, and enterprise-system integration.
Establish and promote software and AI engineering standards across teams, including architecture, data contracts, evaluation, testing, observability, operational excellence, security, privacy, cost management, and responsible AI.
Turn deployment lessons into reusable reference implementations, frameworks, prompts, evaluation approaches, engineering standards, and enablement materials.
Define success measures before implementation and communicate impact across adoption, quality, productivity, capacity created, cycle time, cost, ROI, risk, and sustained business outcomes.
Influence technical direction across teams and stakeholder groups, aligning senior leaders and partners around domain strategy, trade-offs, and measurable outcomes.
Develop engineers, senior individual contributors, and emerging technical leaders; coach senior talent to work independently and extend engineering practices across the organization.