Principal AI Forward Deployed Engineer

Principal AI Forward Deployed Engineer

Introduction to the Team:  

Within CTO Enablement, the AI Enablement and Culture Office is helping Expedia Group become AI-native by turning AI experimentation into measurable business impact—creating capacity, improving speed and quality, and building AI fluency across the enterprise. As a Principal AI Forward Deployed Engineer, you will embed with teams in Business Units/Enterprise functions across EG to identify high-value opportunities, translate ambiguous business challenges into measurable outcomes, and rapidly prototype, productionize, and scale AI-powered solutions.  

You will be a highly influential technical leader who coaches senior individual contributors in leadership and engineering practices, partners with executive leaders, and turns emerging AI capabilities into reusable patterns, tools, and ways of working that teams can adopt. Combining deep hands-on engineering with strong partnership and change leadership, you will help teams adopt AI responsibly and at scale while keeping meaningful outcomes for travelers, partners, and employees at the center. 

In this role, you will: 

• Partner with business-unit and functional leaders, domain experts, end users, and technologists across Expedia Group to discover high-value AI opportunities, define measurable outcomes, and establish pragmatic delivery plans. 

• Operate as a forward-deployed technical leader, partnering directly with business units and their teams to understand workflows, constraints, data, and systems, and owning engagements through delivery, adoption, and value realization. 

• Own the full lifecycle of AI initiatives—from discovery to use-case framing to technical design to rapid prototyping, production deployment, adoption, measurement, value realization 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 with multiple teams, balancing speed to value with reliability, security, privacy, cost, and long-term maintainability. 

• Design highly complex systems that interact across a business unit, applying sound system design, API design, data modeling, observability, evaluation, and testability practices across multiple 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 integration with enterprise systems. 

• Establish and evangelize software and AI engineering standards across multiple teams, including architecture, data contracts, evaluation, testing, observability, operational excellence, security, privacy, cost management, and responsible AI. 

• Convert lessons from individual deployments 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 multiple 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 operate independently and extend engineering practices across the organization. 

Minimum Qualifications: 

• Bachelor’s degree with 12+ years of professional software engineering experience, or a master’s degree with 10+ years of professional software engineering experience or equivalent professional experience. 

• Demonstrated ownership of complex systems, workflows or platforms that support a business unit or function, and integrate 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 within partner business units and their teams to understand ambiguous needs, deliver usable technical solutions quickly, and drive adoption beyond the initial prototype or pilot. 

Preferred Qualifications: 

• 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. 

• Proven ability to shape product and technology direction, develop business-unit and domain-level AI architecture vision, while creating 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.