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Tier-1 employer, metro location, and mid-level experience increase qualified applicant density.
Role demands specialized neuro-symbolic and knowledge-graph research experience, limiting cross-industry transferability.
Explicit 3-year minimum plus mandatory neuro-symbolic, NLP, and graph expertise creates stringent filters.
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Lead research and development of Neuro-Symbolic AI systems integrating knowledge graphs with machine learning and multimodal data.
Design and implement knowledge-driven AI assistants with natural language interaction and explainability capabilities, enhancing robustness and domain adaptability.
Translate research innovations into scalable solutions and collaborate internationally to apply advancements in product engineering and maintenance domains.
Ph.D., M.S., or M.Tech in Computer Science or related fields from top institutes (IITs, IIITs, IISc, etc.).
At least 3 years of relevant professional or applied research experience.
Expertise in Neuro-Symbolic AI architectures combining machine learning and symbolic reasoning (preferably knowledge graphs).
Proficiency in building multimodal data pipelines and practical experience with NLP frameworks and graph libraries such as Hugging Face Transformers, RDFlib, and PyTorch Geometric.
Experienced in designing hybrid AI systems with strong skills in knowledge representation techniques like ontology design and symbolic reasoning.
Capable of formulating and conducting rigorous experiments with reproducible results in research settings.
Ability to integrate large language models and multimodal foundation models using advanced techniques like retrieval-augmented generation.