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
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