Senior Financial AI Engineer (Data & Knowledge Engineering)

Responsibilities:

• Own production-grade AI data processing pipelines for financial content (announcements, news, financial reports, earnings call materials, research reports, etc.), covering text, table, layout, and audio/video processing, as well as chunking, deduplication, clustering, standardization, versioning, and result validation.

• Build layered financial knowledge bases that manage source documents, structured facts, entities and events, full-text and vector indices, and relationship data; maintain metadata including source, time, security, market, language, version, and authorization.

• Engineer and operate RAG services in production—building query processing, permission and time filtering, multi-route retrieval, reranking integration, context assembly, evidence citation, and result return pipelines that ensure traceability to original sources.

• Design extensible AI data processing, knowledge engineering, and indexing frameworks with unified interfaces to adapt to new sources, formats, languages, models, rules, and algorithms; support incremental updates, index rebuilds, historical backfill, deletion, and authorization expiry.

• Integrate LLMs, document understanding models, NLP models, rule systems, and algorithm components into a unified pipeline with clear input/output contracts, task orchestration, version governance, and failure handling.

• Own engineering capabilities for AI data processing and knowledge services: APIs, async tasks, queues, caching, retry and graceful degradation, human review, canary releases, rollbacks, fault recovery, and capacity governance.

• Establish a quality system for AI data processing, knowledge, and RAG—measuring parsing accuracy, retrieval coverage, citation completeness, staleness, latency, stability, and cost, with tiered root-cause analysis.

• Collaborate with data, algorithm, product, and compliance teams to deploy AI data processing and knowledge services reliably in user-facing equities products and Binance AI.

Requirements:

• Master’s degree or above in Computer Science, Software Engineering, AI, or a related field; 5+ years of experience in backend, data platforms, ML engineering, or AI application engineering.

• Familiarity with equity markets and the investor research and decision-making workflow; understanding of trading mechanics, market data, fundamentals, corporate actions, and major market events; ability to assess the entities, time sensitivity, sources, and usage boundaries of financial information.

• Proficient in Python and at least one of Java or another backend language; solid software engineering, distributed systems, and service interface design skills.

• Production experience with LLMs, NLP, or ML systems; ability to explain model invocation, task orchestration, failure recovery, version governance, and online issue resolution.

•  Production experience with knowledge engineering or RAG systems; familiarity with structured, semi-structured, and unstructured content processing; ability to explain the full pipeline from source ingestion through retrieval, citation, and online feedback.

• Familiarity with full-text search, vector search, document storage, and their combinations; understanding of chunking, indexing, filtering, recall, reranking, context assembly, citation, and permission control—not tied to any specific database or framework.

• Experience with performance, stability, cost, and observability governance for high-concurrency or large-scale processing systems—beyond model API calls or demo prototypes.

• Experience adapting new data sources or content types; ability to distill source-specific logic into reusable processing capabilities.