Senior AI Engineer – LLM Applications

Cowbell is signaling a new era in cyber insurance by harnessing technology and data to provide small and medium-sized enterprises (SMEs) with advanced warning of cyber risk exposures bundled with cyber insurance coverage adaptable to the threats of today and tomorrow. Championing adaptive insurance, Cowbell follows policyholders’ cyber risk exposures as they evolve through continuous risk assessment and continuous underwriting. In its unique AI-based approach to risk selection and pricing, Cowbell’s underwriting platform, powered by Cowbell Factors, compresses the insurance process from submission to issue to less than 5 minutes.

Founded in 2019, Cowbell is based in the San Francisco Bay Area with employees across the U.S., Canada, U.K. and India and is backed by over 15 A.M. Best A- or higher rated reinsurance partners.

Position Overview:

Key Responsibilities

• Design and implement RAG-based systems, integrating LLMs with vector databases, search pipelines, and knowledge retrieval frameworks.
• Develop intelligent AI agents that automate tasks, retrieve relevant information, and improve user interactions.
• Work with APIs, embeddings, and multi-modal retrieval to enhance AI application performance.
• Optimize inference pipelines and improve LLM serving, fine-tuning, and distillation for efficiency.
• Stay ahead of the curve by researching and implementing state-of-the-art advancements in generative AI and retrieval techniques.
• Collaborate with stakeholders and cross-functional teams to understand business needs and develop impactful ML models and AI-driven automation solutions.

Minimum Qualifications

• Master's in Computer Science, Data Science, AI, Machine Learning, or a related field (or a Bachelor's degree with substantial experience).
• 5+ years of experience in machine learning, deep learning, and NLP for real-world applications.
• 1+ years of hands-on experience with LLMs and generative AI.
• Expertise in RAG architectures, vector search, and retrieval methods.
• Proficiency in Python and experience with LLM APIs (OpenAI, Hugging Face, Anthropic, etc.).
• Experience integrating LLMs into real-world applications (e.g., chatbots, automation, intelligent decision-making agents).
• Solid foundation in machine learning, statistical modeling, and AI-driven software development.
• Knowledge of prompt engineering, few-shot learning, and prompt chaining techniques.
• Strong software engineering skills, including experience with cloud platforms (AWS) and ML model deployment.
• Excellent problem-solving skills, communication abilities, and ability to work independently.

Preferred Qualifications

• Proficiency in PyTorch or TensorFlow for deep learning model development.
• Experience in LLM fine-tuning, model compression, and optimization.
• Familiarity with frameworks like LangChain, LlamaIndex, or Ollama.
• Experience with multi-modal retrieval systems (text, image, structured data).
• Contributions to open-source AI projects or published research in AI/ML.