Artificial Intelligence Developer

Senior Manager, RAG/LLM Specialist

Experience: 47 Years

Role Overview: Lead the design and optimization of advanced RAG pipelines and model fine[1]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.