SENIOR/LEAD MACHINE LEARNING ENGINEER
Pubblicato il 18-09-2026 - Ntt Data Europe & Latam in Bari
As a Senior Machine Learning Engineer at NTT DATA, you will work alongside experienced Data Scientists, Data and ML Engineers on advanced machine learning and Generative AI initiatives. Your Contribution In This Role Apply hands‐on Generative AI capabilities, preferably on Azure/GCP and on‐premise Gen AI architectures and MLOps Leverage a strong mathematical background Work on classification, information retrieval, clustering and optimization problems Establish scalable, efficient and automated processes for large‐scale data analysis Contribute to model development, model validation and model implementation Identify business opportunities Design and create new data pipelines from scratch, from experiments to production deployment Manage multiple projects Lead ML Engineers Connect with stakeholders The ideal profile At least 5 years of production experience working in Data Science or Software Engineering; Deep knowledge of math, probability,
statistics and algorithms; At least 6/12 months of experience in Generative AI deployment and underlying architecture handling; Vector Database knowledge is well appreciated; Understanding of data structures, data modeling and software architecture; Fluent in at least two mainstream programming languages (Python, Scala, Java, C++); Experience in building an infrastructure for technical users, such as Data Scientist, ML practitioners or data consumers/producers; Strong knowledge of Spark, Databricks is a strong plus; Experience developing/deploying ML solutions in one of the public cloud platforms and on a cross‐cloud base, Snowflake knowledge is a plus; Deep knowledge with machine learning frameworks (such as Keras or Py Torch); Ability to design and implement machine learning pipelines in a production environment; Experience with deployment including knowledge of CI/CD, containerization, and related concepts with a focus over MLOps/Re‐Training/Drift Management; Ability to train more junior team members in multiple Machine Learni