Description

You will design and implement Retrieval-Augmented Generation (RAG) systems using Large Language Models (LLMs) to enhance AI capabilities.

This role is on-site.

Responsibilities

• Develop RAG systems utilizing LLMs to enhance AI capabilities.
• Optimize LLM-based application performance using vector stores and semantic search.
• Conduct experiments to fine-tune LLMs for specific use cases and real-world applications.
• Research latest advancements in machine learning and NLP to improve internal methodologies.

Required Skills

• 5+ years of experience in applied machine learning with a focus on NLP and LLMs.
• Strong understanding of Retrieval-Augmented Generation (RAG) implementation.
• Proficiency with vector stores and semantic search technologies.
• Experience with machine learning frameworks such as PyTorch or TensorFlow.
• Knowledge of fine-tuning techniques to adapt LLMs to specific domains.
• Strong Python programming skills.
• Familiarity with libraries and APIs including Hugging Face Transformers, LangChain, LlamaIndex, or OpenAI.

Preferred Skills

• Experience with AWS or Google Cloud platforms.
• Understanding of MLOps practices for production deployment and maintenance.
• Familiarity with MLX framework, Apple Silicon training/inferencing, reinforcement learning, computer vision, or recommendation systems.

Key Skills

Education

Any Graduate