Expert Backend Engineer (LLM, Big Data Product Application) - Data Infra Team

About the team Shopee’s Data Infrastructure team builds the company’s stable, efficient, secure, and easy-to-use big data infrastructure and platform, supporting data collection, storage, batch and real-time computation, instant query, data development, governance, and visualization for business teams, data teams, analysts, machine learning teams, and BI teams. Public job postings also describe it as a one-stop internal big data platform serving large-scale users and use cases. On top of this foundation, we are building the next generation of AI-native data products — enabling users to ask questions in natural language and receive executable SQL, dashboards, and trustworthy insight / analysis reports directly. Responsibilities Own the architecture, development, and productionization of core AI data product capabilities, including natural language to SQL, dashboard generation, and insight / report generation. Build production-grade LLM application pipelines for enterprise data scenarios, including: metadata and schema retrieval semantic layer and metric understanding query planning and SQL generation SQL validation, rewriting, and execution visualization spec generation narrative insight generation Design and implement reliable backend services and platform capabilities that balance accuracy, access control, explainability, observability, latency, and cost. Deeply understand user workflows in analytics and data consumption, and translate ambiguous business questions into scalable, reusable engineering systems. Work closely with Data Infra, Data Engineering, BI, Frontend, Design, and AI / ML engineering teams to deliver products from zero to one and scale them from one to many. Establish and continuously improve the LLM evaluation and feedback loop, including offline benchmarks, online metrics, user feedback, prompt / model versioning, failure analysis, and quality improvement. Drive performance, reliability, and engineering excellence across the system, and contribute to long-term architectural evolution. Stay current with best practices in LLM applications, AI agents, semantic analytics, and enterprise AI systems, and turn them into practical production solutions. Requirements Bachelor’s Degree or above in Computer Science, Software Engineering, Data Engineering, or related fields. Minimum 6 years of experience in backend engineering, platform engineering, data infrastructure, or AI application engineering, with the ability to own complex system design and core module delivery. Strong computer science fundamentals, including data structures and algorithms, operating systems, networking, databases, and distributed systems. Proficient in at least one backend language such as Go, Java, or Python. Strong hands-on experience building LLM-powered applications, with solid understanding of Prompt Engineering, RAG, Tool Calling, Agents, evaluation frameworks, inference optimization, and guardrails. Strong understanding of data and analytics products, including SQL, data modeling, data warehouses / lakehouses, OLAP systems, semantic layers, metrics systems, dashboards, and reporting. Familiarity with one or more enterprise data / big data technologies such as Spark, Flink, Kafka, Trino / Presto, StarRocks, ClickHouse, Hive, or similar systems. Strong problem-solving and abstraction skills, with the ability to convert ambiguous requirements into robust and extensible technical designs. Strong product sense and user empathy; able to think beyond model capability and understand end-to-end user workflows. Strong communication and cross-functional collaboration skills. [Preferred Qualifications] Experience building production-grade Text-to-SQL, AI Copilot, AI BI, analytics assistants, or automated dashboard / report / insight generation systems. Experience building large-scale internal data platforms, self-service analytics platforms, or enterprise data products. Experience in leading internet companies on AI-related initiatives, and addi…