Job Description:
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.