In this role, you’ll drive the adoption of Generative AI across PayPal’s Site Reliability Engineering organization by building intelligent, scalable platforms—like AI-powered chatbots and predictive systems—that enhance system reliability and engineering productivity. Your work will enable automation at scale, reduce operational toil, and deliver real-time insights that empower engineers across the business.
As a technical leader in SRE Labs, you’ll shape how reliability engineering evolves at PayPal, influencing architecture, best practices, and product direction. You’ll be part of a collaborative, high-performance team that brings cutting-edge technology into mission-critical environments.
Your day to day
In yourday to dayroleyou will
•Lead the design and development of GenAI-driven platforms, including intelligent chatbots, AI copilots, and automation tools for Site Reliability use cases.
•Build and deploy machine learning models in production using technologies like Python, ,LangChain, LLM APIs (OpenAI, Anthropic, Azure OpenAI),
•Collaborate cross-functionally with product, infrastructure, and SRE teams to translate reliability pain points into scalable AI/ML-powered solutions.
•Develop scalable front-end and backend services using React.js and Node.js to build user-facing AI-powered applications and APIs.
•Drive system design and architecture decisions that ensure scalability, reliability, and maintainability of AI platforms.
•Mentor engineers, share best practices, and help evolve PayPal’s GenAI strategy and engineering standards.
What do you need to bring -
•A Bachelor’s orMaster’s degree in Computer Science, Engineering, or a related field—or equivalent practical experience.
•8+ years of experience designing, developing, and deploying AI/ML solutions, with a strong emphasis on LLMs, chatbots, and scalable architecture.
•Proficiency in Python, React.js, and Node.js, with hands-on experience building scalable applications and services.
•Hands-on expertisewithLangChain,,lang graph, RESTful APIs and cloud platforms including deploying models in production.
•Strong understanding of system design principles with experience architecting large-scale, fault-tolerant distributed systems.
•Solid knowledge of ML pipelines, prompt engineering, retrieval-augmented generation (RAG), and integration of LLMs with enterprise systems.
•A proven ability to lead cross-functional projects, mentor peers, and influence technical direction while working in Agile, fast-paced environments.
•You have good analytical and problem-solving skills.
•Strong verbal and written communication skills.
•Flexibility and willingness to learn new technologies and adapt quickly.
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