We're seeking an experienced LLM/ML Specialist with deep expertise in LLaMA models or other open source and Retrieval-Augmented Generation (RAG) systems. The ideal candidate will have strong skills in model fine-tuning, prompt engineering, and production deployment of language models. You'll build and optimize RAG pipelines, implement vector databases, and develop efficient inference solutions. Requirements include 2+ years of LLM experience, Python proficiency with PyTorch/Hugging Face, and demonstrated projects involving LLaMA models.   Technical Skills: Deep understanding of LLaMA model architecture and its variants Experience fine-tuning and adapting LLaMA models for specific applications Proficiency in Python and ML frameworks (especially PyTorch and Hugging Face) Knowledge of prompt engineering specific to LLaMA models Experience with efficient inference and quantization techniques for LLaMA Understanding of model deployment and optimization for large language models Expertise in Retrieval-Augmented Generation (RAG) systems and architectures Experience implementing vector databases and similarity search techniques Knowledge of document chunking and embedding strategies for RAG Familiarity with evaluation metrics for RAG systems Qualifications: Machine Learning, NLP, Computer Science, or related field 2+ years of experience working with large language models, preferably LLaMA Strong mathematical background in statistics and probability Demonstrated projects involving LLaMA model adaptation or deployment Excellent communication skills to explain complex concepts Proven experience building and optimizing RAG pipelines in production environments Preferred: Experience with PEFT methods (LoRA, QLoRA, etc.) for LLaMA models Experience with fine-tuning Understanding of model limitations and ethical considerations Experience with LLaMA integration into production systems Familiarity with open-source LLM ecosystems Experience with hybrid search methodologies (dense + sparse retrieval) Knowledge of context window optimization techniques for RAG systems Experience with multi-stage retrieval architectures Responsibilities: Design and implement LLaMA-based solutions for real-world applications Develop, optimize, and deploy RAG systems using vector databases and embedding strategies Fine-tune LLaMA models using techniques like LoRA and QLoRA Create efficient prompt engineering strategies for specialized use cases Implement and optimize inference pipelines for production environments Design evaluation frameworks to measure model and RAG system performance Collaborate with engineering teams to integrate LLMs into product infrastructure Research and implement the latest advancements in LLM and RAG technologies Document technical approaches, model architectures, and system designs Mentor junior team members on LLM and NLP best practices What We Offer: Opportunity to work on cutting-edge LLM and RAG applications Access to computational resources for model training and experimentation Collaborative environment with other ML/AI specialists Flexible work arrangements with remote options Competitive salary and comprehensive benefits package Professional development budget for conferences and courses Clear career progression path for AI specialists Chance to contribute to open-source LLM ecosystem Balanced workload with dedicated research time Inclusive culture that values diverse perspectives and innovative thinking