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Popular mid-level data role in a metro with generalist title increases applicant competition.
Specialized knowledge-representation skills are transferable, but healthcare supply-chain preference raises moderate sensitivity.
Mandatory ML/LLM, knowledge-graph, and entity-resolution experience with explicit years makes screening highly strict.
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Design and implement knowledge representation solutions including entity resolution, knowledge graphs, and embedding-based semantic similarity systems for healthcare supply chain data.
Develop and operate LLM-assisted knowledge extraction pipelines and uncertainty quantification frameworks to measure confidence and reliability of outputs.
Collaborate with data quality engineers and stakeholders to integrate feedback loops and improve data and model quality in production KR pipelines.
4+ years of applied ML and data science experience including entity resolution, semantic matching, or knowledge graph construction.
Hands-on experience with LLM knowledge extraction techniques (prompt design, structured parsing, fine-tuning).
Working knowledge of RDF and SPARQL; strong programming skills with ML frameworks (PyTorch, HuggingFace, scikit-learn) and graph data tools (RDFLib, NetworkX).
Location: Hyderabad, India (Hybrid). Educational degree: Not explicitly mentioned in the JD. Notice period: Not explicitly mentioned in the JD.
Expert in knowledge representation concepts with familiarity in OWL, description logics, and ontological reasoning to interpret ML outputs accurately.
Proven ability to operate and maintain ML models and data pipelines in multi-disciplinary, production environments bridging formal systems and statistical methods.
Experience with neurosymbolic AI, healthcare supply chain data, and production-scale graph database platforms is a strong plus.