RAG/LLM Engineer

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/TensorFlow, LangChain, LlamaIndex, advanced embedding models. GenAI Skills: Deep expertise in advanced RAG (HyDE, parent-document retrieval), prompt optimization, and parameter-efficient fine-tuning. Qualifications: Bachelors/Master’s in CS/Data Science with 4–7 years in ML/AI, including 1+ years specifically working with LLMs