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.

· Stay curious and up to date with trends of technologies, advanced analytics, genAI and cloud platforms (Databricks).

Must-Have:

· 5+ years of experience in machine learning 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.

English: C1