• Design and build our central MLOps Platform covering the complete ML lifecycle • Architect robust CI/CD workflows and self-service capabilities • Partner with Data Scientists, ML Engineers, and Infrastructure teams • Establish and evangelize MLOps best practices across the organization • Optimize infrastructure costs and enhance observability

• Desarrollar, mantener y optimizar modelos de Machine Learning aplicados a sistemas de recomendación y selección de creatividades publicitarias. • Trabajar sobre el recommendation engine para identificar qué anuncios funcionan mejor según cliente, campaña, audiencia y contexto. • Analizar datos de performance para detectar patrones, oportunidades de mejora y nuevas hipótesis de modelado. • Proponer, validar e implementar nuevos enfoques que mejoren la toma de decisiones dentro de la plataforma. • Participar en el ciclo de vida completo del modelo: análisis, feature engineering, entrenamiento, deploy, monitorización y mantenimiento. • Colaborar con el equipo creativo para entender sus necesidades y traducirlas en soluciones técnicas basadas en datos. • Trabajar junto al equipo de Machine Learning, MLOps y tecnología para llevar modelos a producción de forma robusta, escalable y mantenible.

• The Principal Machine Learning Engineer is responsible for building Machine learning models based on diverse business requirements, setting up the pipelines, and assisting in delivering thoughtful experiences for our partners. This role works in partnership with cross-functional teams to contribute to the development of cutting-edge ML solutions. • Builds/ Optimizes machine learning models. • Researches, analyzes, and documents findings. • Assists in delivering production grade machine learning services that power the ConnectWise platform and products. • Designs and maintains machine learning infrastructure. • Informs, influences, supports, and executes on product decisions and product launches. • Works with cross-functional teams to ensure that proper data pipelines are established to ensure availability of high-quality data.

• Lead and mentor a team of MLOps engineers, fostering technical growth and a culture of operational excellence • Define and drive the MLOps roadmap, aligning infrastructure capabilities with Research, Engineering and product objectives • Establish best practices, standards, and processes for ML infrastructure, deployment, and operations • Own technical decision-making for ML infrastructure architecture and tooling choices • Architect and oversee scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks • Drive the design and implementation of robust model serving infrastructure using platforms like Triton, TorchServe, TensorFlow Serving, and KServe • Define inference architecture strategy optimized for ultra-low latency and high throughput • Design and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data • Collaborate with research teams to bridge the gap between experimentation and production • Define logging, alerting, and monitoring strategy to track model performance, drift, and system reliability