LLM Scientist (Nagpur)

Role Overview We are looking for an experienced LLM Scientist to research, design, and prototype advanced Generative AI systems. This role is experimentation- and coding-heavy, with a strong focus on Agentic AI design, RAG architecture, LLM fine-tuning, and rapid Python-based prototyping of new GenAI techniques ahead of production handoff. Key Responsibilities Agentic AI & GenAI Prototyping - Design and prototype Agentic AI / Multi-Agent systems using LangGraph, LangChain, and MCP. - Research and experiment with emerging LLM and Agentic AI techniques. - Rapidly prototype and validate current GenAI approaches using Python. - LLM Fine-Tuning & Experimentation - Fine-tune LLMs using LoRA/QLoRA. - Conduct experiments comparing models, prompting strategies, and architectures. - Run controlled experiments, including A/B tests and ablation studies. - Apply statistical rigor when designing experiments and interpreting results. - RAG Architecture - Architect and iterate on RAG pipelines. - Experiment with chunking strategies, embeddings, hybrid search, and reranking. - Evaluate retrieval quality and optimize retrieval strategies. 1. Evaluation & Benchmarking - Build evaluation and benchmarking frameworks for GenAI systems. - Evaluate retrieval quality, groundedness, hallucination, and relevance. - Implement observability and tracing using tools such as LangFuse. - Develop robust evaluation methodologies and avoid metric gaming. Machine Learning & Statistical Analysis - Apply statistical concepts including hypothesis testing, confidence intervals, significance testing, and sample-size considerations. - Design and evaluate supervised, unsupervised, and semi-supervised learning approaches. - Analyze bias-variance tradeoff, regularization, and overfitting/underfitting. - Apply classical ML algorithms and evaluation metrics to support GenAI system development. - Collaboration & Research - Track emerging LLM and Agentic AI research and assess applicability to business problems. - .