AI Application Engineer

【Key Responsibilities】

<p>1. Build and Maintain AI-Powered Biomedical Knowledge Systems</p> <p>Develop and maintain biomedical knowledge graphs by integrating data from literature, patents, chemical, and biological databases</p> <p>Apply advanced OCR, and LLM models to automate the extraction, structuring, and summarization of drug discovery and development data</p> <p>Design Retrieval-Augmented Generation (RAG) pipelines to support internal search and chatbot</p> <p>2. Design AI Agents for Drug Discovery and Competitive Intelligence</p> <p>Develop intelligent agents to assist in literature mining, competitive landscape analysis, target-disease linkage, and experimental design optimization</p> <p>Construct intelligent compound and bioassay data management platforms to support medicinal chemistry and pharmacology teams</p> <p>Enable AI-based reasoning capabilities to assist internal stakeholders in hypothesis generation and validation</p> <p>3. Drive Integration of AI in Drug R&D Workflows</p> <p>Collaborate closely with biology, chemistry, and clinical R&D teams to identify high-impact use cases and implement AI solutions</p> <p>Deliver AI prototypes and tools that improve R&D efficiency, quality, and innovation</p>

【Qualifications】

<p>Required:</p> <p>Master’s degree or above in Computer Science, Artificial Intelligence, Bioinformatics, Computational Biology, or a related field</p> <p>2+ years of experience in AI/ML application development, preferably in the biomedical or pharmaceutical domain</p> <p>Strong programming skills in Python; proficient with ML/DL frameworks such as PyTorch or TensorFlow</p> <p>Practical experience with Retrieval-Augmented Generation (RAG), LLM-based architectures, or semantic search systems</p> <p>Preferred:</p> <p>Hands-on experience developing knowledge bases or knowledge graphs in the life sciences domain</p> <p>Familiarity with biomedical data standards, ontologies, and medical/bioinformatics databases</p> <p>Experience working with multi-modal data (text, image, structure) from public or proprietary drug discovery datasets</p> <p>Background in cheminformatics, computational chemistry, or related fields is a strong plus</p>