Manager, RAG/LLM Specialist

On-siteSeniorML Engineerposted 2w ago

✦Role summaryAI-generated

Lead the design and optimization of advanced RAG pipelines and fine-tune open-source LLMs using PEFT/LoRA techniques. Build automated evaluation frameworks with RAGAS and architect hybrid search strategies in vector databases like Pinecone and Milvus while mentoring junior team members.

Skills required

RAG LLMs Fine-tuning Peft Lora Evaluation Ragas Vector Pinecone Milvus Prompt engineering Python PyTorch TensorFlow LangChain LlamaIndex Generative AI Data science AI

About this role

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-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.

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