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.
Responsibilities
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.
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-effective 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.