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