Senior Staff AI Engineer

Overview As a Senior Staff AI Engineer at Nokia, you will spearhead the design, development, and scaling of advanced AI/ML systems that interpret complex, dynamic datasets. Your work will encompass deep semantic analysis of Nokia’s data, large-scale forecasting, and the creation of cutting-edge AI solutions that seamlessly integrate with core telecom technologies. In this leadership role, you’ll guide a team of talented machine learning engineers and collaborate cross-functionally with product, design, operations, and platform teams to shape the future of AI innovation at Nokia.

Qualifications

10-years full-time Software Engineering work experience, including 4-years total technical software engineering experience in ML areas.

Programming language (e.g. C, C++, Java, Python, or Go)

Large-scale training using data structures and algorithms

Modern machine learning algorithms (e.g., tree-based techniques, supervised, deep, or probabilistic learning)

Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib

4+ years of people management experience

Have excellent communication skills and is a team player.

Responsibilities

As a Senior Staff AI Engineer, you\'ll be responsible for building and scaling a diverse range of AI/ML systems that make sense of vast, complex, and ever-evolving data. This includes everything from deep semantic understanding of Nokia data to large-scale forecasting and novel AI applications that integrate directly with Telecom core capabilities.

You\'ll guide a team of talented ML engineers and collaborate cross-functionally with product, design, operations, and platform teams to shape the future of Nokia.

Lead the development and optimization of machine learning models.

Deploy and maintain ML solutions in production environments.

Monitor and evaluate the performance of deployed models, making necessary adjustments.

Shape long-term product and technical vision for the team.

Collaborate cross-functionally to drive technical direction in systems and modeling frameworks.

Communicate vision effectively, representing engineering in leadership forums and strategic discussions.

Drive operational excellence by establishing metrics for product and engineering performance.

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