Job
Description
You are a Senior or Staff AI Scientist responsible for leading the development of domain-specific business ontologies and knowledge engineering infrastructure. Your expertise will be utilized to leverage cutting-edge LLMs, GenAI, and symbolic reasoning to establish an ontology layer, which will serve as the cornerstone of the data platform. This initiative aims to facilitate intelligent data harmonization, enrichment, and decision automation at a large scale. Your key responsibilities will involve spearheading the research and development of ontology learning systems by leveraging state-of-the-art LLMs, structured data mining, and semantic reasoning techniques. You will be tasked with constructing and expanding domain-specific knowledge graphs that seamlessly integrate both external and internal data sources. Additionally, you will drive innovation in semi-automated ontology construction, concept disambiguation, and alignment using GenAI, contrastive learning, and knowledge distillation methodologies. Collaboration across departments is crucial in embedding ontology-driven intelligence into various pipelines, applications, and decision systems. You will work closely with data platform engineers, AI scientists, and product teams to ensure seamless integration and implementation of these intelligent systems. Furthermore, you will be responsible for defining core metrics to evaluate the quality of ontologies, knowledge coverage, and subsequent performance enhancements such as data harmonization and semantic search capabilities. Your role will also involve collaborating with ML infrastructure teams to optimize representation formats (RDF, Knowledge graphs, etc.) and enable scalable, low-latency retrieval and reasoning processes. To qualify for this position, you should hold a PhD or Masters degree in Computer Science, AI, Data Science, or a related field with a focus on knowledge representation, natural language understanding, or data systems. Moreover, you should possess a minimum of 8 years of experience in applied AI or ML research with a specific focus on ontologies, knowledge engineering, entity resolution, or semantic systems. Your expertise should extend to working with LLMs (e.g., GPT, LLaMA, PaLM, Claude) for knowledge extraction, generation, or few-shot learning. A strong background in data engineering, including schema mapping, metadata, master data management, or data pipelines at scale, is also essential. You are expected to have a deep understanding of semantic data models such as ontologies and hands-on experience with relevant libraries or frameworks. Proficiency in programming languages like Python, as well as frameworks like PyTorch or TensorFlow, and graph technologies such as Neo4j, RDFLib, SPARQL, etc., will be beneficial in fulfilling the requirements of this role.,