Job
Description
Role Overview: Cowbell is revolutionizing the cyber insurance industry by leveraging technology and data to offer small and medium-sized enterprises (SMEs) advanced warning of cyber risk exposures along with adaptable cyber insurance coverage. With a focus on adaptive insurance, Cowbell continuously assesses policyholders' cyber risk exposures and underwrites policies in less than 5 minutes using their unique AI-based underwriting platform powered by Cowbell Factors. They have experienced rapid growth since their founding in 2019, operating in the U.S., Canada, U.K., and India, and recently received a significant investment from Zurich Insurance. Key Responsibilities: - Design and implement RAG-based systems, integrating LLMs with vector databases, search pipelines, and knowledge retrieval frameworks. - Develop intelligent AI agents to automate tasks, retrieve relevant information, and enhance user interactions. - Utilize APIs, embeddings, and multi-modal retrieval techniques to improve AI application performance. - Optimize inference pipelines, LLM serving, fine-tuning, and distillation for efficiency. - Stay updated on the latest advancements in generative AI and retrieval techniques and implement them. - Collaborate with stakeholders and cross-functional teams to address business needs and create impactful ML models and AI-driven automation solutions. Qualifications Required: - Master's degree in Computer Science, Data Science, AI, Machine Learning, or related field (or Bachelor's degree with significant experience). - Minimum of 5 years experience in machine learning, deep learning, and NLP for real-world applications. - At least 1 year of hands-on experience with LLMs and generative AI. - Expertise in RAG architectures, vector search, and retrieval methods. - Proficiency in Python and familiarity with LLM APIs (OpenAI, Hugging Face, Anthropic, etc.). - Experience integrating LLMs into practical applications such as chatbots, automation, and decision-making agents. - Strong foundation in machine learning, statistical modeling, and AI-driven software development. - Knowledge of prompt engineering, few-shot learning, and prompt chaining techniques. - Proficient in software engineering, including cloud platforms like AWS and ML model deployment. - Excellent problem-solving skills, communication abilities, and the capacity to work independently. Additional Details about the Company: Founded in 2019 and headquartered in the San Francisco Bay Area, Cowbell has expanded its operations to the U.S., Canada, U.K., and India. The company has received substantial backing from over 15 A.M. Best A- or higher rated reinsurance partners. Cowbell's mission is to redefine how SMEs navigate cyber threats by providing innovative cyber insurance solutions backed by advanced technology and data analysis. Please visit https://cowbell.insure/ for more information about Cowbell and their offerings. Role Overview: Cowbell is revolutionizing the cyber insurance industry by leveraging technology and data to offer small and medium-sized enterprises (SMEs) advanced warning of cyber risk exposures along with adaptable cyber insurance coverage. With a focus on adaptive insurance, Cowbell continuously assesses policyholders' cyber risk exposures and underwrites policies in less than 5 minutes using their unique AI-based underwriting platform powered by Cowbell Factors. They have experienced rapid growth since their founding in 2019, operating in the U.S., Canada, U.K., and India, and recently received a significant investment from Zurich Insurance. Key Responsibilities: - Design and implement RAG-based systems, integrating LLMs with vector databases, search pipelines, and knowledge retrieval frameworks. - Develop intelligent AI agents to automate tasks, retrieve relevant information, and enhance user interactions. - Utilize APIs, embeddings, and multi-modal retrieval techniques to improve AI application performance. - Optimize inference pipelines, LLM serving, fine-tuning, and distillation for efficiency. - Stay updated on the latest advancements in generative AI and retrieval techniques and implement them. - Collaborate with stakeholders and cross-functional teams to address business needs and create impactful ML models and AI-driven automation solutions. Qualifications Required: - Master's degree in Computer Science, Data Science, AI, Machine Learning, or related field (or Bachelor's degree with significant experience). - Minimum of 5 years experience in machine learning, deep learning, and NLP for real-world applications. - At least 1 year of hands-on experience with LLMs and generative AI. - Expertise in RAG architectures, vector search, and retrieval methods. - Proficiency in Python and familiarity with LLM APIs (OpenAI, Hugging Face, Anthropic, etc.). - Experience integrating LLMs into practical applications such as chatbots, automation, and decision-making agents. - Strong foundation in machine learning, statistical modeli
