Senior Financial AI Engineer (Data & Knowledge Engineering)
New
B
BinanceFinancial AI
Asia / Taiwan, Taipei / Hong KongFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonJavaMachine LearningNLPDistributed Systems
Requirements
- Master’s degree or above in Computer Science, Software Engineering, AI, or a related field.
- 5+ years of professional experience in backend, data platforms, ML engineering, or AI application engineering.
- Deep familiarity with equity markets, investor research workflows, trading mechanics, and corporate actions.
- Proficiency in Python and at least one additional backend language such as Java.
- Proven experience building production-grade LLM, NLP, or ML systems.
- Production experience with knowledge engineering or RAG systems, covering ingestion to online feedback.
- Strong understanding of full-text search, vector search, document storage, and retrieval optimization techniques.
- Expertise in designing for performance, stability, cost, and observability in large-scale processing systems.
- Experience in distributed systems and service interface design.
- Ability to adapt to new data sources and distill complex logic into reusable frameworks.
Responsibilities
- Own production-grade AI data processing pipelines for financial content including text, table, layout, and audio/video processing.
- Build layered financial knowledge bases managing source documents, structured facts, entities, and relationship data.
- Engineer and operate production RAG services, including query processing, multi-route retrieval, reranking, and evidence citation.
- Design extensible AI data processing and indexing frameworks to support diverse sources, formats, and historical backfills.
- Integrate LLMs, NLP models, and rule systems into unified pipelines with robust task orchestration and version governance.
- Own engineering capabilities including API design, caching, fault recovery, canary releases, and capacity governance.
- Establish comprehensive quality systems for AI data and RAG retrieval accuracy, latency, and cost efficiency.
- Collaborate with data, algorithm, product, and compliance teams to deploy reliable AI services for equities products.
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