Role Overview:Lead the design and optimization of advanced RAG pipelines and model finetuning processes. Bridge the gap between prototype and enterprise-scale LLM deployment.

Key Responsibilities
Pipeline Ownership:Design and manage complex, multi-stage RAG pipelines ensuring low latency and high relevance.
Model Optimization:Lead fine-tuning initiatives (PEFT/LoRA) for open-source models to
improve domain-specific task performance.
Advanced Evaluation:Develop automated evaluation frameworks (e.g., RAGAS) to continually measure LLM accuracy, context precision, and recall.
Vector Strategy:Architect metadata filtering and hybrid search strategies within vector
databases (e.g., Pinecone, Milvus).
Team Mentorship:Guide junior analysts in prompt engineering, chunking strategies, and code quality.
Tech Stack:Python, PyTorch/TensorFlow, LangChain, LlamaIndex, advanced embedding models.
GenAI Skills:Deep expertise in advanced RAG (HyDE, parent-document retrieval), prompt optimization, and parameter-productive fine-tuning.
Qualifications:Bachelor’s/Master’s in CS/Data Science with 4–7 years in ML/AI, including 1+ years specifically working with LLMs.

📌 Manager, Rag/llm Specialist Gurugram
🏢 EXL Talent Acquisition Team
📍 Gurugram