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Tier-1 brand, mid-level AI role, metro location, and broad LLM/RAG skillset increase competition.
Role demands specialized legal/regulatory LLM and KG experience, so background transferability is limited.
Explicit 4–8 years requirement plus many mandatory LLM, RAG, and compliance skills drives strict filtering.
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Design and implement advanced GenAI and agentic AI applications focused on legal, regulatory, and compliance document workflows to support complex understanding, reasoning, and decision support.
Build and optimize RAG pipelines and document ingestion/retrieval strategies with emphasis on precision, traceability, and reducing hallucinations in AI-generated outputs.
Establish AI quality assurance measures including retrieval evaluation, hallucination detection, and compliance requirements; oversee end-to-end model lifecycle including training, deployment readiness, and monitoring.
4-8 years of experience as AI Engineer developing LLM-powered solutions targeting legal, regulatory, compliance, or policy documents.
Mandatory skills: Strong hands-on expertise in Python (primary) and familiarity with NLP/NLU, GenAI/LLM application development, agentic AI, RAG, embeddings, Knowledge Graphs (RDF/SPARQL), LangChain, LangGraph, AI QA/evaluation frameworks.
Educational Qualification: BE/B.Tech or Equivalent Degree.
Experience with AI quality assurance practices and model lifecycle management in regulated environments is necessary.
Deep experience applying AI/LLM techniques specifically in legal, regulatory, compliance, or policy document domains where auditability and explainability are critical.
Proven ability to architect and operate complex AI workflows integrating retrieval, citation tracking, semantic search, and knowledge graph reasoning for high accuracy and traceability.
Comfortable managing and optimizing multi-step AI pipelines using recent frameworks like LangChain or LangGraph in production or near-production environments.