Role: LLM Scientist

(research, design, prototype advanced Generative AI systems)

Focus: Agentic AI design, RAG architecture, LLM fine-tuning, rapid Python prototyping

Key Responsibilities

- Design/prototype Agentic AI / Multi-Agent systems using LangGraph, LangChain, MCP.
- Fine-tune LLMs using LoRA/QLoRA; run controlled experiments (A/B tests, ablation studies).
- Architect and iterate RAG pipelines (chunking, embeddings, hybrid search, reranking).
- Build evaluation frameworks for GenAI (groundedness, hallucination, relevance) and implement observability (e.g., LangFuse).
- Apply statistical rigor: hypothesis testing, confidence intervals, significance testing, sample-size considerations.

Required Skills

- Solid Python coding; build experimental pipelines/tooling from scratch.
- Deep understanding of LLMs, Transformer architecture, and GenAI fundamentals.




- Hands-on experience with Agentic AI, RAG, and LLM fine-tuning.
- Proficiency in LangChain, LangGraph, MCP.
- Experience with vector DBs: FAISS, ChromaDB, Pinecone.
- Strong grasp of ML/stats: distributions, hypothesis testing, bias-variance, regularization, overfitting/underfitting.
- Model evaluation metrics: Precision, Recall, F1, ROC-AUC; classical ML (regression, classification, clustering, ensembles).

Good to Have

- NLP techniques (BERT, embeddings, sentiment analysis).
- Classical ML (XGBoost, anomaly detection).
- Research background/publications in NLP/LLMs.
- Experience in regulated enterprise domains.
- B.Tech/M.Tech in CS/IT or related field.

📌 LLM Scientist (Nagpur)
🏢 Nice Software Solutions
📍 Nagpur