Position: Software Dev Cloud/AI Intern
Number of Position(s): 1
Duration: 10 weeks
Date: June 2nd, 2025 to August 8th, 2025
Location: Naperville, IL - Hybrid
 

The team you'll be part of

You will be part of the Cloud Mobility Manager (CMM) within Nokia's CNS Division.  The CMM delivers a converged packet core solution that addresses 5G (AMF), 4G LTE (MME), and even 2G/3G in a fully cloud-native architecture. Invent 5G with us, and be part of a diverse, multi-site world-class R&D team!

Education Recommendations

Currently a candidate for a Bachelor’s or Master's degree in Computer Science, Computer Engineering, Data Science, or a related field with an accredited school in the US.

You have:

Python proficiency, C++/C language knowledge Introduction/base knowledge of Machine Learning and Artificial Intelligence concepts with the ability to demonstrate and explain an Exploratory Data Analysis (EDA) process - end to end. Web knowledge (server/client side) and proven ability to code-client/server-side web apps (framework is flexible – i.e. Node/React, Angular, Django, etc)

It would be nice if you also had:

Networking IP Knowledge (IPv6/IPv4), familiarity with Linux, Git, For AI projects: Jupyter Notebooks, MLFlow, AirFlow, and Scikit Learn.   Experience with Kubernetes or other container platforms/projects is preferred.

 

As a part of our team, you will: 

Work on technologies to improve development & prototyping in Virtualization/Cloud Computing including technologies like:

Container Technology/Kubernetes platforms:

Artificial Intelligence and Machine Learning pipelines Automation technologies (workflow automation and unit test development, etc). Cross-functional projects including pieces of software development, test framework, and system deployment.
 

Contribute to a project in ONE of the following areas:

Lab Data/System deployment and automation. (Python, Cloud Infrastructure (Kubernetes), lab/server and networking) Machine learning pipeline: Coding in Python (possibly C++/C) which will be used in the implementation of models for data pipeline (training, serving, testing) Ingest information via a web interface/web browser or other tool to feed the data pipeline.