Senior 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.

What you'll do

• 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 9 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.
• Experience leading a team of backend engineer.
• 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 additional experience in foundation model or AI-native product companies.
• Familiarity with semantic layers, metrics layers, data governance, query security, grounding, explainability, and citation / traceability mechanisms for trustworthy analytics.
• Domain experience in e-commerce, ads, search, recommendation, risk, or business analytics.
• Strong technical leadership with the ability to drive architecture decisions and raise engineering standards across the team.