Senior AI Engineer

Senior AI Engineer

Model development & working to transform and fine-tune existing LLMs

Key Responsibilities

• Dataset Creation and Management (with Sales & Conversational Focus): Lead the design, development, and curation of high-quality datasets specifically tailored for building robust AI models. This includes data collection strategies, data cleaning, feature engineering, and ensuring data integrity and suitability for various modeling tasks. Crucially, this will involve creating and annotating conversational datasets for sales interactions, including intent recognition, entity extraction, dialogue states, and effective sales responses.
• Model Development & Deployment: Architect, develop, train, and deploy advanced AI/Machine Learning models for various applications. This will involve selecting appropriate model architectures (e.g., deep learning, traditional ML), optimizing model performance, and ensuring scalability and reliability in production environments.
• Conversational Sales AI Development (Core Focus): Design, build, and optimize a sophisticated conversational AI agent specifically for selling health insurance products. This includes:
• Developing dialogue management systems that guide users through the sales funnel.
• Implementing intent recognition and entity extraction models tailored to health insurance queries and sales interactions.
• Crafting compelling and compliant sales responses that address customer needs, handle objections, and provide accurate product information.
• Ensuring the AI can effectively qualify leads, explain complex policy details, and guide users towards conversion.
• Prompt Engineering & Dialogue Flow Optimization: Design and optimize prompts for large language models (LLMs) to achieve desired outcomes for specific business use cases, with a strong emphasis on natural, persuasive, and effective sales conversations. This includes developing robust strategies for managing complex conversation flows, ensuring seamless transitions, and maintaining context throughout the interaction.
• Evaluation Criteria & Validation: Define and implement rigorous evaluation criteria across the entire AI pipeline, from dataset creation to model validation. This includes establishing metrics for data quality, model performance, bias detection, and ensuring robust validation strategies to build trustworthy and reliable AI systems. For conversational AI, this will also include metrics for dialogue success, user satisfaction, conversion rates, and adherence to sales scripts/compliance.
• Data Science & Engineering Excellence: Apply strong data science principles to analyze complex data, extract insights, and formulate data-driven solutions. Demonstrate proficiency in data engineering practices, including building scalable data pipelines, managing data infrastructure, and ensuring efficient data access for AI development.
• Research & Innovation: Stay abreast of the latest advancements in AI, machine learning, and fintech/insurance domains, actively exploring and integrating new technologies and methodologies to enhance our AI capabilities, particularly in the realm of conversational sales.