Key Responsibilities:

• Create scalable, efficient AI-based applications and systems utilizing large language models (LLMs), deep learning, neural networks, and NLP techniques to solve real-world insurance challenges.
• Design, implement, and optimize GenAI models and systems tailored for the insurance sector, such as GenAI-powered chatbots, claims prediction, fraud detection, mortality and morbidity modeling, assumption setting, and automated risk assessment and underwriting.
• Develop and manage AI models from proof-of-concept to production, ensuring seamless integration and deployment on cloud platforms.
• Utilize generative AI techniques, such as LLMs, to develop innovative solutions for enterprise industry use cases.
• Perform data analysis, data preprocessing, and feature engineering to prepare datasets for machine learning models.
• Train, validate, and fine-tune machine learning models, ensuring they meet performance and accuracy requirements.
• Deploy AI models into production environments and monitor their performance, making adjustments as necessary to maintain optimal operation.
• Manage the data flow and infrastructure for effective AI deployment.

Qualifications:

• Bachelor's degree in Data Science, Computer Science, Mathematics, Statistics, or a related discipline.
• 3+ years of experience in data science, focusing on AI/ML applications
• Knowledge of deep learning, large language models (LLMs), and machine learning algorithms, especially natural language processing (NLP).
• Expertise in AWS cloud services (e.g., SageMaker, Lambda, ECS, ECR, S3, IAM) and solid understanding of cloud infrastructure.
• Strong proficiency in Python and/or R, along with experience in Machine Learning frameworks like scikit-learn, TensorFlow, PyTorch, and NLP/GenAI tools such as NLTK, Hugging Face Transformers, LangChain, LlamaIndex.
• Solid understanding of AI concepts and techniques, not requiring extensive coding or modeling expertise.
• Good communication skills and the ability to collaborate closely with both business and technology teams.
• Problem-solving mindset and critical thinking ability.