Head of Artificial Intelligence – Machine Learning

Job Description: • Design and build GT’s AI and ML platform ecosystem, spanning ML Platform, AI Platform, Data Platform, and applied modeling layers that power personalization, recommendations, and intelligent automation. • Establish systems for model training, deployment, monitoring, and evaluation at scale, ensuring reliability and repeatability across teams. • Lead the implementation of LLM and agentic frameworks, including vector embeddings, evaluation pipelines (evals), and orchestration systems to support both product and internal AI capabilities. • Architect and oversee the development of production-grade AI systems — from experimentation to live deployment. • Partner with engineering and data teams to integrate ML and generative AI models into GT’s platform and consumer experiences. • Champion MLOps best practices, enabling fast iteration and safe deployment cycles for data and model pipelines. • Define and execute GT’s AI/ML roadmap, ensuring alignment with company vision and product goals. • Collaborate cross-functionally with product, data, and infrastructure leaders to identify opportunities for AI innovation in personalization, discovery, pricing, and content generation. • Partner with leadership to develop ethical AI standards, governance frameworks, and performance metrics that scale responsibly. • Recruit, mentor, and grow a world-class team of ML engineers, data scientists, and AI platform developers. • Foster a culture of technical excellence, curiosity, and cross-disciplinary collaboration. • Establish strong feedback loops between research, engineering, and product to accelerate innovation. Requirements: • Bachelor’s, Master’s, or Ph.D. in Computer Science, Machine Learning, Data Science, or a related field. • 8–12+ years of experience in AI/ML engineering, including 3–5 years in technical leadership roles. • Strong background in machine learning capabilities. For example, this could include product recommendation engines, ranking problems, or dynamic pricing systems, etc • Experience influencing platform development for providing foundational machine learning components for data scientists to deliver into production • Deep knowledge of software architecture and engineering best practices, especially modern cloud computing stacks for deploying machine learning and microservices at scale especially on Snowflake Benefits: • Positive work culture • Professional development opportunities Apply tot his job