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

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 Boschs 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 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 symbolicneural 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.

Remote Work :

No

Employment Type :

Full-time