Role Overview We are looking for an experienced LLM Scientist to research design and prototype advanced Generative AI systems This role is experimentation and codingheavy with a strong focus on Agentic AI design RAG architecture LLM finetuning and rapid Pythonbased prototyping of recent GenAI techniques ahead of production handoff Key Responsibilities Agentic AI GenAI Prototyping Design and prototype Agentic AI MultiAgent 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 FineTuning Experimentation Finetune 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 samplesize considerations Design and evaluate supervised unsupervised and semisupervised learning approaches Analyze biasvariance 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