Descripción de la oferta
Azure Machine Learning Engineer 5+ years Key Responsibilities / Functional Skills: · Expand the scope of Advanced Analytics and AI, enabling cases that leverage the latest emerging technologies, fostering an innovative environment. · Work closely with the Data Scientists of the Advanced Analytics & AI Center of Expertise to design and develop Machine Learning and Generative AI models, also collaborating with data engineers and data analysts. · Deploy and optimize the AI models that leverage large-scale data to deliver predictive and analytical capabilities for the AST&I domains, delivering scalable and efficient solutions. · Build and maintain end-to-end ML pipelines, ensuring model reproducibility, scalability, and monitoring in alignment with best practices for MLOps (feature engineering, model training, evaluation, and deployment in production environments). · Engage in complex long-term projects, with focus on continuous delivery in small increments, with possibility of effectively leading and planning the projects to ensure successful outcomes. · Engage and guide non-technical stakeholders and team members on Advanced Analytics and GenAI cases. · Collaborate with multidisciplinary teams and manage different stakeholders. · You will be part of diverse Agile/Scrum DevOps team and have end-to-end responsibility, for developing, managing, and maintaining functionalities in the AST&I area, which are prioritized by the Product Owner. · Sta· 5+ years of experience in machine learny curious and up to date with trends of technologies, advanced analytics, genAI and cloud platforms (Databricks). Must-Have: ing engineering or applied ML with a focus on Azure cloud technologies. · Proficient in Python (preferably OOP), PySpark and strong experience with ML frameworks such as Scikit-learn, TensorFlow, PyTorch. · Hands-on experience with Databricks platform tools. · Solid understanding of data preprocessing, feature engineering, and model optimization. · Experience with ML pipeline orchestration using tools like MLflow, Azure Machine Learning. · Excellent understanding of evaluation metrics and ML evaluation methods such as A/B testing and cross-validation. · Experience with Git and building CI/CD pipelines for ML models, preferably in Azure DevOps/Azure Pipelines. Good to have: · Familiarity with Generative AI models and the open-source frameworks for GenAI such as LangChain. · Knowledge of vector databases. · Experience with monitoring and logging ML models in production. · Knowledge of data governance and compliance for ML use cases. · Databricks certifications. · Azure certifications.
