Description

Lead the development of the GenAI product platform, including StreamSync, RAGCore, and DocForge. Own the architecture and optimization of Retrieval-Augmented Generation (RAG) systems for enterprise documents.

Responsibilities

• Architect scalable ML pipelines for analyzing Solution Architecture Documents (SADs).
• Design and optimize RAG systems for processing enterprise architecture data.
• Develop advanced NLP solutions for document analysis and knowledge extraction.
• Manage and mentor ML scientists while driving technical decisions for infrastructure and model selection.
• Lead ML model evaluation, optimization, and deployment strategies.

Required Skills

• 5+ years of experience in machine learning.
• Expert knowledge of Python and ML frameworks (PyTorch or TensorFlow).
• Deep expertise in Natural Language Processing (NLP) and Large Language Models (LLMs).
• Hands-on experience with LangChain and Llama-index.
• Practical knowledge of RAG systems and vector embeddings.
• Experience with AWS and Azure cloud platforms.
• Proficiency in API development and containerization.
• Understanding of MLOps practices and vector databases.
• Master's degree in Computer Science, Machine Learning, or a related field.

Key Skills

Education

Master's degree