Requirements

Led or built an ML Engineering or model lifecycle automation team at a high-growth company
System-level and mathematical depth to design model factory architecture, review training pipelines, and debug inference latency issues
Experience in high-throughput environments with tight latency constraints and high financial risk from model failures
Proven track record of hiring and developing senior engineers and building teams
Ability to navigate ambiguity and make architecture decisions with incomplete information
Strong technical skills across deep learning, ML systems, and production infrastructure to lead through expertise

Nice to Haves

Experience at a tier-1 fintech or payments company
Experience with graph-based methods (GNNs, entity resolution) in production
Foundation model fine-tuning or LLM evaluation experience
Experience establishing ML engineering practices during transitions from classical ML to deep learning

Architect the declarative pipeline (“Model Factory”) for deploying and monitoring hundreds of ML models
Build the Experimentation Engine to connect research to high-throughput production for various model architectures
Develop infrastructure for automated retraining, drift detection, threshold management, and audit trails for large-scale safe operation
Recruit, lead, and mentor a world-class team of ML engineers, establishing a high-performance engineering culture
Define and manage cross-platform partnerships to govern build versus consume decisions for the team’s pillar