Research Engineer Neuro-Symbolic AI & Multimodal Assistant Systems

Roles & Responsibilities:

• Conduct cutting-edge research in Neuro-Symbolic AI focusing on the integration of formal knowledge representations (e.g. ontologies knowledge graphs logic-based systems) with modern machine learning and deep learning techniques.

• Design develop and optimize multimodal data pipelines that combine text vision audio sensor and structured data to support knowledge-driven Neuro-Symbolic AI systems.

• Architect and implement hybrid reasoning and analytical engines capable of complex problem-solving including causal reasoning planning explainable decision-making and symbolic-neural inference.

• 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. from top Indian institutes (IITs IIITs IISc etc.) in Computer Science or a related field (e.g. NLP linguistics artificial intelligence cognitive science)

Experience:

3-5 years

Mandatory/required 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 (e.g. knowledge graphs rule engines logic programming ontologies) 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

• Hands-on Experience with Natural Language Processing for Knowledge Systems: Practical experience in applying state-of-the-art NLP techniques (e.g. Transformers LLMs information extraction) to understand user queries extract insights from text and contribute to the automatic construction and expansion of dynamic knowledge bases.

• Proven ability to work collaboratively in cross-functional and international teams.

Preferred Skills:

• Strong understanding of knowledge representation and reasoning techniques including ontology design schema alignment rule authoring explainable inference workflows and hybrid symbolic-neural evaluation methodologies.

• Hands-on experience with enterprise knowledge graph and semantic web technologies especially Stardog 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.

• 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