• Lead the architecture and evolution of scalable, high-performance data pipelines and ML systems, focusing on data ingestion, transformation, quality checks, and enrichment. • Provide technical leadership and mentorship to a cross-functional team of ML Engineers, Data Scientists, and Infrastructure Engineers, ensuring alignment with architectural standards and driving a culture of high quality and operational excellence. • Drive cross-functional initiatives to integrate modern Machine Learning and AI technologies (including semantic understanding, natural language processing, and potentially large language models) to automate data quality, link canonical products, and create intelligent data enrichment solutions. • Define strategies to enhance the performance, reliability, and observability of data and ML services, ensuring robust, high-quality data outputs. • Design and implement frameworks for evaluating data quality and the effectiveness of ML models through both offline metrics and online validation. • Champion engineering best practices and mentor engineers across teams, raising the bar for code quality, data governance, and ML system design. • Shape long-term technical direction by staying ahead of trends in AI, ML, data engineering, and distributed systems and bringing these innovations into production within the Knowledge domain.

• Design and implement scalable machine learning pipelines, serving prompt engineering workflows to enhance scalability and efficiency in submission intake processes across multiple insurance use cases. • Collaborate cross-functionally, serving as a technical lead for mid and senior team members, providing mentorship and guidance to elevate team performance and technical knowledge. • Ensure production-grade deployment standards, emphasizing scalability, reliability, and compliance with insurance data handling policies, balancing rapid iteration with stability. • Build reusable, modular infrastructure components and CI/CD pipelines for ML and LLM workloads, enabling rapid experimentation and seamless transition from research to production. • Champion best practices in observability, testing, and monitoring of ML systems, establishing standards for model/data drift detection, logging, and automated rollback strategies. • Partner with Data Science leaders to shape the vision and direction of MLOps at Federato and the future of our products

• Develop, implement, and validate machine learning and agentic flows to improve submission intake workflows, underwriting decision making, and related predictive/automation tasks. • Establish, maintain, and evolve the principles guiding autonomous agent behavior and decision-making across AI workflows. • Lead the design of benchmarking frameworks, evaluation tools, and metrics to continuously measure, upgrade, and optimize models, LLMs, and related systems for latency, predictive accuracy, and coverage of relevant use cases. • Build modular, reusable model architectures, training routines, and evaluation frameworks that enable rapid experimentation and adaptation to multiple insurance use cases. • Lead the research, design, and implementation of novel machine learning models and agentic workflows that drive next-generation product capabilities.

• Design and implement systems that combine rules, models, feature engineering, and business and product inputs into an email detection product. • Identify and recommend new features groups or ML model approaches that can significantly improve detection efficacy for a product. • Understand features that distinguish safe emails from email attacks. • Train models on well-defined datasets to improve model efficacy on specialized attacks • Actively monitor and improve False Positive rates and efficacy rates for our message detection product attack categories.