RAI Scientist

• Proficient in multiple machine learning (ML) and deep learning (DL) frameworks such as TensorFlow and PyTorch, along with expertise in programming languages like Python, R, SQL, Scala, and Julia. • Demonstrated proficiency in deploying Large Language Models (LLM) and Generative AI applications, with knowledge of techniques like Prompt Engineering Fine-Tuning (PEFT) and Retrieval-Augmented Generation (RAG). Experience in developing and deploying deep neural networks and ML models for intricate tasks. Familiarity with basic tooling for MLOps, LLMops, AI security, adversarial testing, and more. • Develop and construct platforms and tools tailored for Responsible AI principles such as fairness, security, and explainability across various model types (ML/DL/LLMs), data formats, and lifecycle stages. • Implement solutions and methodologies within existing AI projects to serve as safeguards against diverse model vulnerabilities, including toxicity and adversarial attacks. • Collaborate with business analysts, engineers, and stakeholders to ensure alignment of data science initiatives with Responsible AI principles. • Assist in devising and executing rigorous adversarial testing protocols for AI models to uphold Responsible AI standards. • Explore cutting-edge techniques, architectures, and methodologies to automate adherence to Responsible AI practices throughout the AI lifecycle, spanning from data preparation to model deployment and inferencing. • Establish monitoring systems to track model performance over time and institute mechanisms for regular model updates and maintenance. • Share insights and expertise across the organization to foster thought leadership and innovative strategies for addressing various aspects of Responsible AI.