RAG​/LLM Specialist

Position: RAG / LLM Specialist
Role Overview :

Lead the design and optimization of advanced RAG pipelines and model fine tuning 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.

Required

Skills & Qualifications

Tech Stack:
Python, PyTorch/Tensor Flow, Lang Chain, Llama Index, advanced embedding models. GenAI

Skills:

Deep expertise in advanced RAG (HyDE, parent-document retrieval), prompt optimization, and parameter-efficient 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.