Senior Manager, AI Platform Engineering

Senior Manager, AI Platform Engineering WHY SOCURE? Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day. We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading. At Socure, our AI Platform team turns cutting-edge models into real-world systems, serving customers at massive scale. We’re seeking a Senior Manager of AI Platform Engineering to lead the team responsible for building, scaling, and operating the systems that power our entire ML lifecycle. If you are a technical leader who understands distributed systems, values strong platform design, and is motivated by enabling data scientists and engineers to deliver models to production with speed, reliability, and confidence, this might just be the place for you. You will define the roadmap for our ML platforms and tooling, guide engineering execution, establish best practices, and ensure that Socure’s model development and deployment processes are secure, governed, built for scale, and best-in-class. KEY RESPONSIBILITIES PLATFORM VISION & STRATEGY - Develop and own the roadmap for Socure’s AI/ML platform, including data and feature engineering workflows, training infrastructure, experimentation tooling, model deployment/serving, monitoring, and governance. - Define architecture and standards that create clear, scalable, and secure paths for building and operating AI systems. - Assess technology options and drive consolidation across the company to reduce fragmentation and improve consistency across the ML toolchain. - Partner with Data Science, Engineering, Product, and Sales-Enablement teams to develop AI infrastructure that delights Customers. ML LIFECYCLE OWNERSHIP - Lead the design and operation of the end-to-end ML lifecycle: data ingestion, feature engineering, experimentation, training, model registry, deployment, and continuous monitoring. - Partner closely with Data Science to enable fast, reproducible experimentation and reduce operational friction. - Ensure the platform delivers reliability, traceability, observability, and performance for both batch and real-time model workloads. EXECUTION & DELIVERY - Guide the team to deliver high-quality platform capabilities with predictable timelines and strong technical rigor. - Remove cross-team bottlenecks, align dependencies, and ensure seamless execution across Data, Infrastructure, and Product. - Establish SLAs, operational standards, and production-readiness guidelines for ML pipelines and serving systems. GOVERNANCE, COMPLIANCE & RISK MANAGEMENT - Implement and enforce best practices around model versioning, auditability, lineage tracking, data governance, and security controls. - Partner with Security, GRC, and Compliance to ensure ML processes meet regulatory expectations and support safe, responsible AI usage. - Oversee processes for model certification, performance monitoring, and lifecycle management. LEADERSHIP & TEAM DEVELOPMENT - Lead, mentor, and grow both senior and junior ICs across ML infrastructure, MLOps, and distributed systems. - Build a culture of technical excellence, accountability, and continuous improvement. - Recruit top engineering talent and support career development through coaching and structured feedback. QUALIFICATIONS REQUIRED - 8+ years of professional software engineering experience, including time spent building or operating large-scale ML, data, or distributed systems platforms. - 3+ years of engineering leadership experience ... (truncated, view full listing at source)