Lead Machine Learning Engineer

Join our Trust Intelligence Platform organization as a Lead Machine Learning Engineer. In this role, you will define and execute the technical ML strategy, manage multiple ML pipelines, and collaborate with cross-functional teams. You will also provide technical leadership in ML engineering best practices and mentor junior machine learning engineers.

- Definir y ejecutar la estrategia técnica de ML, con un enfoque en arquitecturas de modelos robustas y prácticas de MLOps. - Liderar el desarrollo de extremo a extremo de pipelines de ML, centrándose en flujos de trabajo de reentrenamiento automatizados y optimización de modelos. - Colaborar con equipos de Ciencia de Datos, Ingeniería de Datos, Investigación y Gestión de Productos para entregar soluciones de ML escalables.

- Deep understanding and practical application of containerization (Docker) and workflow orchestration (Kubernetes, Apache Airflow) for automated ML pipelines - Mastery of Python programming, including proficiency in leading ML frameworks (TensorFlow, PyTorch) and adherence to software engineering best practices - Track record of publications and/or patents in quantitative disciplines - Significant experience in developing and deploying generative AI solutions into production environments - Masters or PhD in a quantitative field - Solid foundation in feature engineering techniques and the implementation of feature stores - Expertise in advanced Natural Language Processing (NLP) methodologies - Preferred skills: - Successfully led machine learning initiatives, consistently delivering significant and quantifiable business outcomes - Previous experience in a mentoring role for junior engineers - Experience in formulating ML governance policies and ensuring adherence to data security regulations - Demonstrated success in implementing comprehensive MLOps methodologies, encompassing CI/CD pipelines, testing protocols, and model performance monitoring - Exceptional collaboration abilities, with a strong capacity to work effectively across Data Science, Platform Engineering, Research and Product teams - Expertise in infrastructure-as-code principles, monitoring tools, and big data technologies (Spark, Snowflake) - Demonstrated experience conducting research or working collaboratively with Machine Learning (ML) research teams - Extensive experience (6+ years) in the end-to-end lifecycle of multi-model machine learning systems, from design and development to large-scale deployment