Senior AI Platform Engineering Manager, Socure

• 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 managing multiple teams or engineering managers
• Strong technical background in ML infrastructure, data engineering, and/or cloud-native distributed systems
• Demonstrated experience delivering complex, cross-functional platform initiatives
• Excellent communication and stakeholder management skills, with the ability to translate between technical detail and business priorities
• Experience working in fast-paced, iterative environments using modern development practices

Desirable

• 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
• 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
• 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
• 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
• 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
• 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
• Success in This Role Looks Like
• Models ship faster and more reliably, with reduced operational overhead for data scientists and engineers
• The ML platform becomes the standardized, well-adopted path for developing and deploying models
• Platform metrics—latency, uptime, cost efficiency, drift performance, and experiment velocity—show clear improvement
• Governance and auditability are built into platform workflows, not bolted on
• Engineering teams report high clarity, strong alignment, and confidence in platform direction