• Own the lifecycle of our computer vision models, including training, evaluation, deployment, and iteration • Improve model performance in real-world conditions (noise, edge cases, data drift) • Design and improve data pipelines that process thousands of miles of sidewalks and millions of images • Lead architectural decisions for handling hundreds of terabytes of geospatial and visual data, including storage layout, pipeline reliability, and inference performance • Set and evolve best practices around deployment, observability, system reliability, and scalability • Act as a senior individual contributor and mentor, helping raise the technical bar across the engineering team • Work closely with the CEO and operations team to turn business needs into clear technical priorities

• Design and implement end-to-end ML solutions from experimentation to production; • Build scalable ML pipelines and infrastructure; • Optimize model performance, efficiency, and reliability; • Write clean, maintainable, production-quality code; • Conduct rigorous experimentation and model evaluation; • Troubleshoot and resolve complex technical challenges. • Mentor junior and mid-level ML engineers; • Conduct code reviews and provide constructive feedback; • Share knowledge through documentation, presentations, and workshops; • Collaborate with cross-functional teams (DevOps, Data Engineering, SAs); • Contribute to internal ML practice development. • Stay current with ML research and emerging technologies; • Propose improvements to existing solutions and processes; • Contribute to the development of reusable ML accelerators; • Participate in technical discussions and architectural decisions.

• Diseñar e implementar soluciones basadas en LLMs (GPT, Claude u otros). • Construir agentes y flujos conversacionales, gestionando contexto, prompts y memoria. • Integrar servicios de IA a través de APIs (OpenAI, Anthropic, Azure AI). • Trabajar con datos estructurados y no estructurados para enriquecer las capacidades del sistema. • Iterar sobre modelos y prompts en base a feedback de usuarios y métricas. • Optimizar performance, calidad de respuestas y costo de inferencia. • Asegurar buenas prácticas de seguridad, privacidad y uso responsable de IA. • Colaborar con equipos de producto y negocio para definir casos de uso y soluciones.

• Design, train, and deploy machine learning models across use cases such as recommendation systems, churn prediction, fraud detection, and behavioral scoring • Build modular ML pipelines for feature engineering, training, validation, and inference • Implement deep learning and NLP models, including transformer-based architectures for text and sequence data • Develop and deploy LLM-based applications • Integrate models into production systems via REST/gRPC APIs and microservices • Implement MLOps pipelines using CI/CD, versioning, and monitoring tools • Monitor model performance, detect drift, and implement automated retraining strategies • Optimize models for latency, throughput, and cost in cloud environments • Work closely with Data Engineers to ensure scalable and efficient data access • Evaluate and integrate new AI frameworks and tools to continuously improve performance