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 robust focus on Agentic AI design, RAG architecture, LLM fine-tuning, and rapid Python-based prototyping of recent 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.
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.
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📌 Llm Scientist Nagpur
🏢 Nice Software Solutions
📍 Nagpur