Research Engineer – Neuro-Symbolic AI (Knowledge Graphs) & Multimodal Assistant Systems

Job Description

Roles & Responsibilities :

Conduct cutting-edge research in Neuro-Symbolic AI, focusing on the integration of formal knowledge representations such as knowledge graphs with modern machine learning and deep learning techniques.

• Design multimodal data pipelines integrating text, structured data, and, where relevant, visual or sensor information.

• Design and develop knowledge-driven AI systems with natural language interaction capabilities, enabling explainable, trustworthy, and human-centered AI assistants.

• Integrate Neuro-Symbolic reasoning with large language models (LLMs) and multimodal foundation models using knowledge grounding, structured retrieval and reasoning-aware workflows to improve robustness, interpretability, and domain adaptability.

• Design, build, and evolve semantic assets — such as ontologies, knowledge graphs, semantic data models, and symbolic rules / constraints — that ground, validate, and explain neuro-symbolic AI systems in production-oriented research settings.

• Prototype, evaluate, and validate research concepts through experiments, benchmarks, and real-world use cases, translating research outcomes into scalable solutions.

• Collaborate closely with international, cross-disciplinary teams of researchers, engineers, and product stakeholders to apply research innovations to business-relevant scenarios such as product engineering, diagnostics, maintenance, and repair.

• Contribute to technology transfer, supporting the transition from research prototypes to production-ready systems in collaboration with software and product teams.

• Publish research findings in top-tier conferences and journals, file patents, and contribute to Bosch’s intellectual property portfolio.

• Stay up to date with the latest advancements in AI, machine learning, knowledge representation, and multimodal systems, ensuring Bosch remains at the forefront of innovation.

• Actively participate in internal and external research communities, workshops, and collaborations to foster knowledge exchange and thought leadership.

Qualifications

Educational qualification:

Ph.D. or M.S. or M. Tech from top Indian or foreign institutes (IITs, IIITs, IISc etc.) in Computer Science or a related field (e.g., NLP, linguistics, artificial intelligence, cognitive science)

Experience :

• At least 3 years of relevant professional or applied research experience.

Mandatory/requires Skills :

• Expertise in Neuro-Symbolic AI Architectures and Frameworks: Proven ability to design, implement, and integrate hybrid AI systems that combine machine learning with symbolic reasoning (preferably knowledge graphs) to address complex requirements.
• Advanced Data Engineering for Multi-Modal Integration: Demonstrated proficiency in building robust data pipelines capable of integrating, cleaning, and preprocessing heterogeneous data sources
• Understanding of core NLP concepts and practical experience using state-of-the-art NLP frameworks, libraries (e.g., Hugging Face Transformers, spaCy, NLTK, Gensim etc.) for text processing, tokenization, semantic parsing, and language modeling.
• Proven experience in extracting structured knowledge from unstructured text using NLP techniques like Named Entity Recognition (NER) and Relation Extraction. to understand user queries, extract insights from text, and contribute to the automatic construction and expansion of dynamic knowledge bases.
• Experience utilizing popular graph libraries (e.g., RDFlib, KGLab, and PyTorch Geometric) to develop and deploy graph-based ML algorithms, including link prediction, node classification, relation extraction and graph embeddings like Node2Vec.
• Use visualization tools like Gephi, D3.js for Graph analytics and visualization.
• Proven ability to work collaboratively in cross-functional and international teams.

Preferred Skills - Any of the following will be an added advantage:

• Strong understanding of knowledge representation and reasoning techniques, including ontology design, schema alignment, rule authoring, explainable inference workflows, and hybrid symbolic-neural evaluation methodologies.
• Neuro-Symbolic AI & Hybrid Reasoning: Experience architecting or implementing hybrid reasoning and analytical engines capable of complex problem-solving, including causal reasoning, planning, explainable decision-making, and symbolic-neural inference. Architect and implement hybrid reasoning and analytical engines capable of complex problem-solving, including causal reasoning, planning, explainable decision-making, and symbolic–neural inference.
• Hands-on experience with enterprise knowledge graph and semantic web technologies, especially Neo4J, Stardog, STEADY including ontology modelling using OWL/RDFS, RDF data modelling, SPARQL query development, SHACL-based validation, reasoning/inference, and graph-based data integration.
• Working knowledge of symbolic query and reasoning approaches such as SPARQL, SHACL, description logics, logic programming, rule-based inference, or constraint-based reasoning.
• Ability to formulate research questions, design experiments, define evaluation criteria, run ablations, and interpret results with scientific rigor and strong reproducibility practices.
• Hands-on experience working with, prompting, fine-tuning, and evaluating Large Language Models (LLMs) and Vision-Language models. Strong understanding of Retrieval-Augmented Generation (RAG) paradigms.
• Skills in graph embeddings, graph neural networks, Answer Set Programming/Prolog, SWRL or Drools, causal reasoning, agentic AI workflows, MLOps/cloud deployment, research publications, and patent drafting will be an added advantage.