• Design, train, fine-tune, and evaluate deep learning, classical ML, and foundational GenAI models to drive core product features. • Build, scale, and maintain robust ML pipelines for continuous training, evaluation, and real-time/batch inference using modern MLOps frameworks. • Collaborate with backend and frontend teams to integrate AI models into microservices, ensuring low latency, high availability, and optimal resource utilization. • Architect scalable data pipelines for preprocessing, vectorizing, and ingestion of massive structured and unstructured datasets. • Implement rigorous evaluation frameworks for model alignment, bias mitigation, guardrailing, and cost/latency optimization (e.g., quantization, distillation). • Provide technical leadership, conduct thorough code reviews, and mentor junior/mid-level engineers on best practices in software craftsmanship and ML engineering.

• Build, train, and optimize computer vision models for image classification, face liveness detection, and presentation attack detection (PAD) / anti-spoofing • Work on real-world identity verification and biometric authentication problems, improving model performance on noisy, adversarial inputs such as spoofed images, replay attacks, deepfakes, and synthetic media • Design and run experiments to improve model accuracy, recall, robustness, and fraud detection performance using techniques such as augmentation, class balancing, architecture tuning, and hard-negative mining • Design, train, and improve deep learning models (e.g., CNNs, Vision Transformers, and foundation models), including loss function design, hyperparameter optimization, and performance tuning on large-scale image datasets • Prepare and curate large, noisy datasets, including data ingestion, validation, cleaning, deduplication, labeling strategies, and dataset QA to improve model reliability and generalization • Develop evaluation protocols and success metrics that balance fraud detection effectiveness, false acceptance rates, false rejection rates, and overall business impact • Develop production-grade training and inference pipelines on AWS with strong reproducibility, monitoring, observability, and cost controls • Productionize models as resilient Python services and libraries; collaborate with platform teams to optimize APIs, latency, scalability, and operational reliability • Contribute to the evolution of our Identity Verification (IDV) platform by modernizing legacy components and improving model performance, maintainability, and modularity • Partner closely with Product, Customer Success, Fraud, and Platform Engineering teams to ensure ML solutions meet privacy, compliance, security, and reliability requirements • Support and mentor other engineers through design reviews, code reviews, experimentation best practices, and knowledge sharing • Research and evaluate emerging techniques in face liveness detection, presentation attack detection (PAD), deepfake detection, biometric authentication, and adversarial machine learning to strengthen our fraud prevention capabilities

• Tune and optimize ML/DL models for production-scale audio deepfake detection. • Investigate failure cases in the client environment, build custom evaluation frameworks, and implement mitigation strategies for model robustness. • Drive model iteration for performance under a variety of real-world environments, e.g. compression artifacts, noise, telephony, and streaming pipelines. • Present technical findings and model performance insights to internal stakeholders. • Interface with Product and Engineering teams to build a deep understanding of the production environment and incorporate relevant evaluations for performance assessment.

Role Description Estamos en búsqueda de un MLOps Engineer (AWS) para integrarse a un proyecto enfocado en una plataforma de forecasting de demanda y optimización de horarios operando en producción sobre AWS. La persona seleccionada será responsable de garantizar la estabilidad, confiabilidad y escalabilidad de los pipelines existentes, participando activamente en despliegues, monitoreo, automatización y mejora continua de la plataforma. Este rol está orientado a la operación y mantenimiento de soluciones de Machine Learning en producción, más que al desarrollo de modelos desde cero. - Operar y mantener en producción la plataforma de forecasting de demanda y optimización de horarios. - Garantizar que los pipelines de ML corran de forma confiable en AWS, identificando y resolviendo incidentes proactivamente. - Gestionar despliegues de modelos y servicios usando Docker, CI/CD y herramientas de IaC (SAM / CloudFormation). - Refactorizar y mejorar código Python existente para aumentar rendimiento, mantenibilidad y cobertura de pruebas. - Monitorear servicios y pipelines mediante CloudWatch y herramientas equivalentes. - Colaborar con el equipo de data science para integrar nuevas versiones de modelos al entorno productivo. - Participar en revisiones de código (PRs) siguiendo buenas prácticas de Git y pytest. - Documentar configuraciones, decisiones de infraestructura y procedimientos operativos. Qualifications - Python 3.12 sólido: pandas, Polars, numpy, scikit-learn, XGBoost; capacidad de refactorizar código ajeno. - AWS: Lambda, S3, IAM, CloudWatch, Step Functions, SageMaker, ECR. - Docker: build y debug de imágenes. - CI/CD: Azure DevOps o equivalente (GitHub Actions / GitLab CI). - IaC: lectura y edición de SAM / CloudFormation. - Git + pytest: trabajo por ramas, PRs y pruebas unitarias. Requirements - 3–5 años de experiencia en roles de MLOps, DevOps o infraestructura de ML. - Experiencia demostrable manteniendo pipelines de ML en producción sobre AWS. - Perfil proactivo con capacidad de trabajar de forma autónoma. Benefits - 🚀 Integración a marcas globales y startups disruptivas. - 🏡 Trabajo remoto/Home office. - 📍 En caso de requerir modalidad híbrida o presencial, serás informado desde la primera sesión. - ⏳ Horario ajustado a la célula de trabajo/proyecto asignado. - 📅 Trabajo de lunes a viernes. - 🎉 Día off en tu cumpleaños. - 🏥 Seguro de gastos médicos mayores (aplica para México). - 🛡️ Seguro de vida (aplica para México). - 🌎 Equipos de trabajo multiculturales. - 🎓 Acceso a cursos y certificaciones. - 📢 Meetups con invitados especiales del área de IT. - 📡 Eventos virtuales de integración y grupos de interés. - 📢 Clases de inglés. - 🏆 Oportunidades dentro de nuestras diferentes líneas de negocio. - 🏅 Orgullosamente certificados como Great Place to Work.