AI数据研发工程师(J25344)

职位描述

Key Responsibilities: 1. AI-Ready Data Architecture & Strategy: Spearhead the strategic design and implementation of enterprise data pipelines dedicated to delivering high-quality, "AI-ready" datasets. Bridge the gap between raw data and AI applications by ensuring data is structured, standardized, and optimized for consumption by LLM (Large Language Model)/AI Agents via the Algorithm/Development teams. 2. Cross-Functional Synergy & Business Alignment: Act as the primary technical liaison between the data engineering team, business stakeholders, and development/algorithm teams. Deeply understand complex business scenarios and downstream LLM/AI Agent requirements, translating them into robust data architecture designs and actionable engineering roadmaps. 3. Advanced Pipeline & Unstructured Data Leadership: Oversee the development of scalable data platform and pipelines capable of ingesting, cleaning, and transforming massive volumes of multi-source heterogeneous data (especially unstructured/semi-structured pharma R&D data, literature, etc.) into formats suitable for AI Agents (data API, MCP, A2A etc.), and RAG (Retrieval-Augmented Generation) systems. 4. Data Platform & Governance for AI: Lead the construction and management of the company's pharmaceutical R&D data platform architecture design and data assets. Establish rigorous data governance frameworks, data lineage, and quality control best practices to build a trustworthy data foundation that empowers downstream AI models and advanced analytics. 5. Technical Leadership & Mentorship: Build, manage, and mentor a high-performing team of data engineers. Foster a culture of engineering excellence, continuous learning, and agile delivery, while effectively managing resource allocation to support fast-paced AI initiatives.

任职要求

• Qualifications:
• Experience & Leadership: 5+ years of extensive experience in data engineering, data architecture, or related fields, with a proven track record in technical management and leadership.
• AI-Data Expertise: Demonstrated experience in building data infrastructure for Machine Learning or LLM applications. Familiarity with the data preparation lifecycle for AI Agents/LLM applications (MCP, A2A, RAG) is highly required.
• Education & Background: Master’s degree or above (Ph.D. is highly preferred) in Computer Science and Engineering, Data Science, Bio-/Chem-informatics, or highly related STEM fields.
• Technical Mastery: Expert-level proficiency in SQL and Python. Must possess the technical depth to make high-level architectural decisions, conduct code reviews, and guide the team through complex data engineering and AI-integration bottlenecks.
• Tools & Platforms: Deep expertise in the big data ecosystem. Extensive practical experience in architecting complex data pipelines using scheduling tools (e.g., DataWorks, DataBricks, DolphinScheduler, etc.) and modern data stack technologies.
• Comprehensive Abilities: Exceptional strategic thinking and executive communication skills. Capable of taking full ownership of the project lifecycle and seamlessly collaborating with algorithm scientists and researchers to drive business impact.
• Bonus Points:
• Industry Background: Deep domain knowledge and hands-on data processing experience in the Pharmaceutical R&D industry. Candidates with interdisciplinary academic and industrial work experience in Pharma R&D, Biology, Chemistry, or Pharmacy will be given priority consideration.
• Architecture Experience: Solid experience in enterprise data warehouse modeling in an R&D field, and a strong track record of leading large-scale data platform design and applications.