Sr. MTS, AI & Agentic Architect

eBay•San Jose, CA

7d•$156,800 - $290,500•Hybrid

About The Position

eBay’s Customer Experience teams build the technology and platforms that support buyers and sellers across the globe. AI is creating new possibilities for these experiences, and we’re building the technical foundation to bring them to life across eBay. That means solving complex architecture questions around how models connect with enterprise knowledge, tools, and workflows while working seamlessly with the platforms our customers rely on today. We’re looking for a Sr. Member of Technical Staff, AI & Agentic Architect within Global Platforms to help shape what comes next! You’ll lead architecture across agentic systems, orchestration, and platform integration, creating patterns that teams can build on as the technology evolves. This is a hands-on role with the opportunity to influence both the technical decisions we make today and how agentic capabilities evolve across eBay over time. Hybrid Requirement: 3 days in office and 2 days from home. Primary location: Austin, TX; also open to Portland, OR, and Salt Lake City, UT.

Requirements

10+ years of software engineering experience, with significant depth designing and architecting large-scale distributed systems and production platforms.
Proven experience architecting and shipping AI/ML or Generative AI systems into production, with meaningful ownership of architecture, technical decisions, and outcomes.
Strong knowledge of modern AI system architecture, including agentic systems, orchestration, tool use, retrieval-based architectures, conversational AI, or intelligent workflows.
Deep distributed-systems expertise across APIs and service boundaries, event-driven systems, data and state management, scalability, resiliency, latency, and observability.
Hands-on experience integrating AI into complex production environments, connecting models and agents with enterprise knowledge, APIs, tools, workflows, or transactional systems.
Technical depth that extends from architecture into implementation, with experience using prototypes, reference implementations, technical investigation, and design or code reviews to validate important decisions.
Cross-organizational technical leadership, with experience setting direction across multiple engineering teams and influencing senior engineers and cross-functional partners.
Strong engineering judgment when balancing quality, safety, security, performance, operational complexity, and cost at significant scale.

Nice To Haves

Experience with Customer Experience, Customer Service, contact center technology, conversational AI, or voice/IVR platforms is valuable but not required. Specific model providers, vector databases, and agent frameworks will continue to evolve. We value strong architectural fundamentals, production experience, curiosity, and the ability to evaluate emerging technologies and apply them to complex engineering problems.

Responsibilities

Define architecture for agentic Customer Experience capabilities, bringing together models, orchestration, retrieval, tools, workflows, decisioning, policy, evaluation, and human support.
Establish reusable patterns and platform boundaries that help teams build and operate AI-enabled experiences across customer journeys and service channels.
Architect how agents interact with enterprise knowledge, APIs, tools, workflows, case management, and policy systems while meeting high standards for reliability, security, performance, and customer trust.
Shape how agentic systems operate at scale, including approaches to evaluation, observability, resiliency, latency, model selection, and token and infrastructure costs.
Evaluate emerging models, agent frameworks, orchestration approaches, and industry patterns to identify technologies that can strengthen the platform and improve how teams build.
Validate critical architecture decisions through prototypes, technical spikes, reference implementations, and design reviews, turning successful approaches into capabilities that teams can adopt.
Lead technical alignment across teams, navigating complex tradeoffs and connecting architecture decisions to customer needs, engineering priorities, and long-term platform health.