Milano, Bari, Bologna, Cosenza, Napoli, Pisa, Roma, Torino (Italy)
Type
Full Time
Responsibilities:
The ideal candidate as Senior Machine Learning Engineer in NTT DATA will have a solid Generative AI hands-on capability (preferably on Azure/GCP and on-premise GenAI architecture and MLops), mathematical background, experience working on a range of classification, informational retrieval, clustering, and optimization problems, establishing scalable, efficient, automated processes for large scale data analysis, model development, model validation, and model implementation.
The Senior Machine Learning Engineer will work alongside experienced Data Scientists and data and ML Engineers to identify business opportunities, and design and create new data pipelines from scratch, from experiments to deploying them in production.
He/She will be responsible for multiple projects, leading the ML Engineers and connecting with the stakeholders.
Requirements & Skills:
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 Scientists, ML practitioners, or data consumers/producers;
Strong knowledge of Spark, and 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 of machine learning frameworks (such as Keras or PyTorch);
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 on MLops/Re-Training/Drift Management;
Ability to train more junior team members in multiple Machine Learning and Deep Learning concepts;
Establish and maintain strong relationships with internal team members and external clients
