Bosch Group: Research Engineer – Neuro-Symbolic AI (Knowledge Graphs) & Multimodal Assistant Systems — bengaluru, India

About Bosch Group

Bosch's India activities span mobility, industrial technology, consumer goods, and energy and building technology. Its employer description identifies engineering development, manufacturing and commercial operations within that range. The group operates through multiple Indian businesses, including activities in automotive systems, software and engineering, industrial technology and home appliances. This makes Bosch a collection of related technology businesses rather than a single product operation, with different entities serving different industrial and consumer applications.

The position and its setting

Bosch Group lists Research Engineer – Neuro-Symbolic AI (Knowledge Graphs) & Multimodal Assistant Systems in bengaluru, India, under reference REF292400J. The details below separate the work described by the employer from essential conditions, preferred experience and practical preparation advice.

• Employment type: Full-time
• Employer listing date: 27 July 2026
• Contract type: Unlimited
• Legal Entity: Bosch Global Software Technologies Private Limited
• Brands: Bosch Group

Responsibilities and work described

• Research / execution: 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
• Integration: 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
• Collaboration: 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
• Contribution: 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

Essential requirements and candidate background

• 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: 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: 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
• 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
• Ability to work collaboratively in cross-functional and international teams

Preferred experience and additional strengths

• Understanding: 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
• Experience: 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
• Knowledge: symbolic query and reasoning approaches such as SPARQL, SHACL, description logics, logic programming, rule-based inference, or constraint-based reasoning
• Formulate research questions, design experiments, define evaluation criteria, run ablations, and interpret results with scientific rigor and reproducibility practices
• Hands-on experience working with, prompting, fine-tuning, and evaluating Large Language Models (LLMs) and Vision-Language models
• Understanding: 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

Location, schedule and contract conditions

• Work arrangement: On-site
• Advertised location: bengaluru, India

Employer details to confirm

• The employer’s seniority category and its stated experience requirements do not agree. Follow the role-specific requirements above rather than assuming this is a fresher opening.

Conditions requiring the complete notice

Some passages contain complex or combined conditions that have not been reduced to short eligibility statements here. Read the complete linked notice for those passages, including any alternatives, exceptions or restrictions. The concise facts above should not be treated as the full wording of every condition.

Pay and application dates

A verified salary figure is not established in this article. An online estimate for another Research Engineer – Neuro-Symbolic AI (Knowledge Graphs) & Multimodal Assistant Systems vacancy is not an offer for reference REF292400J; use the employer’s own pay disclosure and recruitment communication.

A closing date is not confirmed in this article. Check the current employer page before submitting: an undated posting can change or be withdrawn.

How to apply to Bosch Group

Applications follow the official employer page linked below.

Open the official Bosch Group posting below and match the title and reference REF292400J.

Compare your qualifications and experience with the essential conditions for the bengaluru, India position, then read the preferred criteria separately.

Prepare an accurate CV and the supporting items requested in that employer’s application. Provide only information and documents you are authorised to share.

Use the employer’s application option, complete the required fields and review the submission. Retain the acknowledgement when one is provided. Job Alerts World does not receive the application.

Practical preparation: machine learning

For machine-learning work, describe the problem, the data and the evaluation setup. Explain the baseline, the metric and the part you owned; mention deployment or monitoring only when you worked on it.

Relevant skills

• AI/ML
• LLMs
• machine learning
• AI
• maintenance
• stakeholder management