Data Scientist - Remote

Overview

As a Data Scientist at QAD, you will work on advanced AI/ML models, focusing on RAG, LLMs, prompt engineering, and AI Agents while collaborating with cross-functional teams to enhance AI capabilities and develop innovative solutions.

In Short

• Develop, fine-tune, and optimize Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems.
• Implement AI Agents capable of decision-making and interactive learning.
• Research and experiment with various prompt engineering techniques to improve model performance and accuracy.
• Build and maintain machine learning pipelines for AI/ML models in production environments.
• Work with structured and unstructured data, including text, images, and multimodal datasets.
• Collaborate with software engineers and data engineers to integrate AI models into scalable applications.
• Analyze and interpret model outputs to refine algorithms and improve performance.
• Stay updated on the latest AI and ML research and contribute to internal knowledge-sharing initiatives.

Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field.
• 1-2+ years of professional experience.
• Strong programming skills in Python and familiarity with libraries such as TensorFlow, PyTorch, LangChain, and Hugging Face Transformers.
• Experience with NLP, LLM fine-tuning, and retrieval-based systems.
• Knowledge of prompt engineering techniques, LLM jailbreak methods, and AI Agents.
• Understanding of traditional and modern machine learning algorithms, including deep learning architectures.
• Familiarity with cloud platforms such as AWS, GCP, or Azure for AI/ML deployments.
• Basic understanding of data engineering, ETL pipelines, and model deployment.

Benefits

• Opportunity to work on various projects and technologies with diverse teams.
• Collaboration with cross-functional teams to enhance AI capabilities.
• Access to the latest AI and ML research and development opportunities.
• Flexible working arrangements with a virtual-first company culture.
• Support for professional growth and continuous learning.