Machine Learning

Description

Job Summary: Develop and maintain highly automated ML pipelines and collaborate with software engineering teams to integrate ML workflows. Key Highlights: 1. Development and maintenance of large-scale automated ML pipelines 2. Collaboration with engineering teams to integrate ML workflows 3. Implementation and management of distributed computing infrastructure for ML DESCRIPTION Develop and maintain highly automated ML pipelines for large-scale model training, validation, and deployment. Collaborate closely with software engineering teams to integrate ML workflows into the software development lifecycle, applying DevOps and MLOps practices. REQUIREMENTS * Advanced Python programming skills and knowledge of ML libraries such as TensorFlow, PyTorch, Scikit\-Learn, etc. * SQL * ETL (preferably DataIku) * Analytic workflow tools (Alteryx, Tableau Prep, Data Fusion, etc.) * Database administration * ODBC \- JDBC\- Spark connections. * GCP \- Vertex AI (2 years) * Intermediate \- Conversational English (Mandatory) **Additional:** * Database clients (DBeaver, MySQL, SQL Manager, etc.) * Cloud computing knowledge * Hive * Solid experience in software development and ML operations, with emphasis on MLOps. **Responsibilities:** * Develop and maintain highly automated ML pipelines for large-scale model training, validation, and deployment. * Collaborate closely with software engineering teams to integrate ML workflows into the software development lifecycle, applying DevOps and MLOps practices. * Implement and manage distributed computing infrastructure and orchestration tools to support ML workflows in production environments. * Develop metrics and monitoring tools to evaluate model performance and quality in production. * Automate maintenance and monitoring tasks to ensure continuous stability and reliability of ML systems. * Identify diverse data and information requirements (Sources, Catalogs, KPIs, Dimensions), aligning with current business processes and data sources as well as those available within the Data Analytics architecture, thereby facilitating scoping and proposal of product support. * Propose data integration strategies for products/tools based on business needs to enhance the value of deliverables. * Identify and propose, via generated documentation, areas for improvement in data integration to suggest more efficient solutions for data transformation. * Understand and interpret data meaning, and document this understanding in deliverables from various Data Analytics integration projects, aiming to generate valuable insights for decision-making and to ensure information consistency across products.