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 : Bachelor's/Master's in CS/Data Science with 4 - 7 years in ML/AI, including 1+ years specifically working with LLMs