boschgroup
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. 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 Ensure explainability, auditability, and compliance of AI outputs in regulated environments Support end-to-end model lifecycle activities including experimentation, versioning, deployment readiness, and monitoring handover Core Skills (Must-Have) Python (primary) Docker / Docker Compose NLP / NLU GenAI / LLM application development Agentic AI RAG (Retrieval-Augmented Generation) Embeddings Contextual chunking strategies Knowledge Graphs (RDF) SPARQL Model lifecycle / ML application lifecycle LangChain LangGraph Git AI QA / evaluation (e.g., RAGAS, LLM judges, retrieval and answer quality validation) Nice-to-Have Java Kubernetes Dev Containers GitOps Documentation practices Deepagents (or similar advanced agent frameworks) Domain Advantage Experience with legal, regulatory, compliance, and policy documents Understanding of requirements around auditability, explainability, and risk controls in AI systems Educational qualification: BE/B.Tech or Equivalent Degree Experience : 4-8 Years Mandatory/requires Skills : Strong hands-on expertise in Python (or Java), NLP, RegEx, SpaCy, NLTK, and transformer-based models . Preferred Skills :