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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. 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.
Job Responsibility:
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
Requirements:
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
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
Nice to have:
Experience with ML lifecycle tooling (e.g., Spark, Ray, Kubeflow, MLflow, Chalk, Feast, Airflow) and modern cloud infrastructure (AWS/GCP/Azure, Sagemaker, Kubernetes)
Familiarity with model governance, drift detection, compliance frameworks, or regulated data environments
Background in fintech, identity verification, fraud, or similarly high-stakes ML domains