Enterprise AI Engineer III

Expedia Group•Austin, TX

2d•$116,500 - $163,000

About The Position

The AI & Automation Team onboards and manages AI tools, and partners with teams across Expedia Group to create innovative products and services. We deliver high-quality experiences for employees worldwide with best-in-class technology to boost productivity and enable AI in the flow of work. We're hiring an Enterprise AI Engineer III to own the design, engineering, and continuous improvement of our cloud infrastructure and enterprise AI systems, as part of our growing team. You'll work at the intersection of platform engineering and AI, owning service and integration architectures, driving IaC and automation strategy, and enabling other engineers through high-quality patterns, decision records, and mentorship.

Requirements

5+ years of experience in infrastructure, platform, or enterprise systems engineering (or 3+ years with a Master's degree).
Bachelor's degree or equivalent practical experience in a relevant field; technical degree preferred.
Demonstrated growth mindset and the ability to own initiatives end-to-end — from problem framing through delivery — while coordinating a broad range of stakeholders across platform, security, and business teams.
Hands-on, design-level experience with public cloud IaaS (AWS, Azure, or GCP) — compute, networking, storage, IAM — paired with ownership of IaC automation (Terraform, Pulumi, or equivalent), including building reusable modules and policy-as-code frameworks.
Proficiency in at least one programming language (Python, Go, or similar), with a track record of building automation that reduces operational toil, forecasting capacity, and making cost-aware recommendations.
Experience designing integration architectures for SaaS platforms or enterprise systems, including lifecycle management, security controls, and compliance considerations.
Experience defining SLOs and engineering observability solutions (telemetry, synthetic testing, alerting) that drive measurable operational improvements.
Solid AI/ML literacy, including working knowledge of LLMs, RAG, agentic systems, and AI-assisted operations — with the ability to architect guardrails, governance controls, and monitoring for AI workflows in production.
Experience administering or integrating enterprise AI copilot and knowledge-assistant platforms — including access controls, data integration, and driving adoption across business teams.
Demonstrated ownership of service or integration architecture, including security and operability design decisions from inception through production.
Strong technical communication skills, including the ability to author architecture decision records, design documents, and runbooks that serve as durable references.

Nice To Haves

Experience with enterprise SaaS administration and integration at scale (e.g. Glean, ServiceNow, Salesforce, or similar platforms).
Experience leading or contributing to platform team engineering standards, templates, or golden paths used by multiple teams.
Demonstrated mentorship of less experienced engineers, including code or design review and structured technical guidance.
Contributions to open-source infrastructure tooling or internal developer platforms.
Experience working in an environment where AI/automation is embedded into operational workflows — not just a future roadmap item.
Familiarity with FinOps practices — unit economics, cost attribution, and cloud cost optimization at the workload level.

Responsibilities

Own service and integration architecture for AI platforms and enterprise systems — designing for security, cost-efficiency, and long-term operability from the start.
Design and implement reusable IaC modules and automation frameworks; enforce policy-as-code and reduce operational toil at scale.
Lead incident resolution, RCA, and post-incident analysis across teams; prevent recurrence through automation and architectural improvements.
Design integration architectures connecting internal platforms and enterprise SaaS systems; ensure security, compliance, and scalability of data and workflow flows.
Define SLOs and engineer actionable observability — telemetry pipelines, synthetic tests, and alerting — to give teams the signals they need to operate confidently.
Champion automation and SaaS integration best practices; measure toil reduction and drive adoption across the team.
Design cross-team operational flows; optimize handoffs, runbooks, and escalation paths to reduce failure demand.
Author architecture decision records, technical design documents, and self-service content that enable teams to move faster without creating dependencies on you.
Forecast capacity and cost impacts; surface data-driven recommendations to inform infrastructure investment decisions.
Mentor junior and mid-level engineers; define documentation templates and engineering standards that scale your expertise across the team.