Senior Python Engineer, AI

eBay•Washington, NV

1d•$118,800 - $205,600•Remote

About The Position

WHI Solutions, Inc., an eBay company, operates Nexpart, a B2B marketplace built for professional automotive and truck parts businesses. Nexpart plays a critical role in helping customers efficiently source and purchase the parts they need to keep their operations running smoothly and serve their own customers with speed and confidence. The Nexpart engineering team builds and evolves the technology behind this marketplace. We are seeking a Senior Python Engineer, AI to help design, develop, and maintain scalable backend services, integrations, and platform capabilities that support critical business workflows and ongoing product innovation. We are seeking an engineer with strong Python backend experience and a track record of building reliable, scalable services. In this role, you will work with cross-functional partners to develop practical solutions that support critical workflows and improve the performance and resilience of the platform.

Requirements

BA/BS in Computer Science or a related field, or equivalent practical experience
5+ years of professional Python development experience in production environments
Strong experience building asynchronous, API-driven microservices (e.g., FastAPI, asyncio)
Solid understanding of scalable backend architectures and service-to-service integrations
Experience with distributed systems patterns (queues, caching, pub/sub, background processing)
Strong understanding of REST APIs, authentication/authorization, and secure system design
Experience with CI/CD pipelines and containerized deployment workflows
Strong familiarity with Git and modern version control workflows
Excellent English communication skills, both written and verbal
Ability to work effectively in cross-functional, fast-paced environments

Nice To Haves

Experience integrating LLM APIs in production and building robust, testable AI-enabled services
Familiarity with prompt design, structured outputs, tool/function calling, and conversation/workflow state management
Experience with embeddings, vector databases, retrieval pipelines, and/or ranking approaches
Experience building AI evaluation harnesses (golden datasets, regression testing, quality metrics)
Exposure to ML concepts relevant to applied product systems (classification, extraction, ranking, personalization)
Experience with real-time or conversational interfaces (chat and/or voice) and streaming systems
Proficiency in Spanish (or other major marketplace languages) to help evaluate and validate multilingual AI outputs
Experience supporting high-traffic, consumer-facing applications

Responsibilities

Define technical requirements in partnership with product, business, and engineering stakeholders
Design, develop, and maintain Python-based applications & services that power core platform workflows
Build and integrate scalable APIs and microservices for internal and external consumers
Own backend system design for service reliability, scalability, maintainability, and operational excellence
Optimize performance using distributed technologies (e.g., caching, message queues, event-driven architectures)
Implement robust observability (logging, tracing, metrics), troubleshoot production issues, and improve system resiliency
Participate in code reviews as both a contributor and reviewer; contribute to engineering standards and best practices
Collaborate closely with development, QA, and business teams to deliver high-quality releases
Create and maintain system and technical documentation as needed
Leverage AI-powered development tools to improve productivity, code quality, and delivery speed
Build and productionalize LLM-enabled features (e.g., workflow automation, intelligent routing, summarization, extraction)
Design tool/function-calling services and orchestration layers for multi-step tasks
Implement RAG-style retrieval patterns (embeddings, vector search, ranking) where it improves reliability and accuracy
Develop evaluation and quality frameworks for AI outputs (offline evals, regression tests, safety/reliability checks)
Collaborate on responsible AI practices: guardrails, data handling, deterministic behaviors, and auditability