AI Engineer
Bosch
Company Description
Bosch Global Software Technologies Private Limited is a 100% owned subsidiary of Robert Bosch GmbH, one of the world's leading global supplier of technology and services, offering end-to-end Engineering, IT and Business Solutions. With over 27,000+ associates, it’s the largest software development center of Bosch, outside Germany, indicating that it is the Technology Powerhouse of Bosch in India with a global footprint and presence in the US, Europe and the Asia Pacific region.
Job Description
Experience Summary
- 4–8 years of experience as an AI Engineer focused on building and operating LLM-powered solutions for legal, regulatory, and compliance document workflows (e.g., EU AI Act, DSA, Data Privacy, ESG, Security, Compliance).
- Strong emphasis on reference identification, citation grounding, retrieval quality, traceability, explainability, and evaluation in document-centric AI systems.
Core Responsibilities / Focus
- Design and implement GenAI and Agentic AI applications for complex document understanding, reasoning, and decision support
- Build and optimize RAG (Retrieval-Augmented Generation) pipelines tailored to regulatory and legal documents with high precision and grounded responses
- Develop robust document ingestion and retrieval strategies including contextual chunking, embeddings, metadata enrichment, and semantic indexing
- Implement reference identification, citation tracking, and traceability mechanisms for document-centric AI workflows
- Optimize retrieval ranking, semantic search, and grounding to improve answer accuracy and reduce hallucinations
- Integrate Knowledge Graphs (RDF/SPARQL) with LLM workflows for structured and unstructured reasoning
- Orchestrate multi-step AI workflows using LangChain, LangGraph, or similar agent frameworks
- Establish AI quality assurance and evaluation practices including retrieval evaluation, hallucination detection, LLM judge frameworks, and RAGAS-style scoring
- Build, train, and fine-tune specialized NER and document understanding models
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