Advise enterprise customers on fraud and identity risk, design tailored verification strategies and workflows, and guide implementation from onboarding through launch. Coordinate cross-functional teams, resolve technical and regulatory blockers, validate implementation milestones, and support go-live readiness and execution.
You will define and own the end-to-end automated test strategy for distributed microservices and web portals while mentoring other engineers. The role involves hands-on development of test frameworks, influencing system architecture, and integrating quality practices into the CI/CD pipeline.
The Customer Success Manager will own a portfolio of enterprise and mid-market accounts, acting as a value consultant to ensure measurable risk reduction and identity strategy optimization. They are responsible for monitoring customer health, driving adoption, managing renewals, and identifying expansion opportunities through proactive engagement.
The Principal Enterprise Architect will define the technical vision for scalable, high-performance SaaS platforms while driving the adoption of GenAI and cloud-native engineering practices. This role involves leading the Architecture Review Council, optimizing infrastructure costs, and mentoring engineering teams to ensure architectural coherence and operational excellence.
You will act as a technical expert to guide prospective clients through the implementation of Jumio's identity verification solutions. This involves leading technical presentations, managing customer onboarding, and collaborating with internal teams to improve product features.
Develop and maintain automated test scripts for web and API applications while collaborating with development teams to ensure software quality. Execute functional, regression, and integration tests within CI/CD pipelines to provide actionable feedback and ensure release readiness.
Lead the design and scaling of production-ready computer vision systems for biometric face recognition and attributes. Own the end-to-end ML pipeline from data ingestion and curation to low-latency deployment on AWS.
Lead the design and scaling of production-grade computer vision systems for biometrics, including face recognition and attribute detection. Own the end-to-end ML pipeline from data ingestion and curation to low-latency deployment on AWS.
Lead the design and scaling of production-ready computer vision systems for biometric face recognition and attributes. Own the end-to-end ML pipeline from data ingestion and curation to low-latency deployment on AWS.
Develop and maintain advanced machine learning and computer vision models to detect fraud for identity verification. Deploy these models via AWS SageMaker or on-device and collaborate with product managers in an Agile environment.
Design and develop end-to-end computer vision models and pipelines for biometrics use cases such as face detection and recognition. Optimize models for low-latency inference and mentor other ML engineers on technical best practices.
Build and integrate ROS/ROS2-based modules to support robotic navigation and data collection workflows. Develop and maintain end-to-end machine learning pipelines for training, evaluating, and monitoring model performance in production.
Lead the design and development of computer vision and biometric systems, including face recognition and quality assessment. Own the end-to-end machine learning pipeline from data ingestion and synthetic data generation to production deployment on AWS.
The role involves leading the design and development of computer vision systems for biometrics, including rigorous fairness analysis and benchmarking of models. Responsibilities also include owning end-to-end ML pipelines from data ingestion to deployment and optimizing models for low-latency inference.
