Description :

Role : RAG / LLM Specialist

This track focuses on the core mechanics of Generative AI, specifically optimizing retrieval mechanisms, refining embeddings, and tailoring foundation models to enterprise data.

Senior Manager, RAG/LLM Specialist

Experience : 4 to 7 Years

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., RAGAS) to continually measure LLM accuracy, context precision, and recall.

- Vector Strategy : Architect metadata filtering and hybrid search strategies within vector databases (e., 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/Masters in CS/Data Science with 4 to 7 years in ML/AI, including 1+ years specifically working with LLMs

0 Credits