Summer Intern- AI Support Technician

Job Description

Summer Intern - AI Support Technician, Norcross, GA

Position Summary

OFS Fitel, LLC, a leading manufacturer of optical fiber, fiber cable, and connectivity components has an opening for summer internship in optical fiber connectivity area with AI technology. This role will provide an opportunity for the intern to use AI technology to develop a unique method for fiber splicing operation.

Responsibilities

• Choose right AI Framework:
• Performing general literature survey and find best AI framework to fulfil project goal.
• Selecting the right framework that support this particular project's requirements.
• Gather and Prepare Data:
• Collect relevant data for this project. Ensure it's clean, labeled, and representative.
• Gathering fiber profile brightness data from splicers.
• Changing camera’s focus, brightness intensity, etc. to obtain various brightness data.
• Preprocess the data by handling missing values, scaling, and encoding categorical features.
• Train AI Model:
• Use chosen framework to create and train the model. This involves selecting an appropriate algorithm, setting hyperparameters, and feeding prepared data.
• Iteratively train and fine-tune the model until it performs well on validation data.
• Test and Evaluate AI Model:
• Split data into training, validation, and test sets. Evaluate the model's performance using metrics like accuracy, precision, recall, or F1-score.
• Address any overfitting or underfitting issues.
• Deploy and Monitor AI System:
• Deploy trained model in multiple splicers.
• Continuously monitor AI system's performance, retrain as needed, and stay up-to-date with new data.
• Link the Fiber ID flag to auto recipe selection process in splicers.
  
  

Qualifications

• Undergrade or graduate students majoring in math, computer science or relevant subjects.
• Foundational Knowledge : Mathematics and Statistics, Programming Skills (Proficiency in programming languages such as Python).
• Machine Learning Basics : Supervised Learning, Deep Learning.
• Data Handling and Preprocessing : Data Collection, Data Cleaning.
• Model Selection and Evaluation : Model Selection, Cross-Validation, Evaluation Metrics: (Knowing how to assess model performance - accuracy, precision, recall, F1-score, etc.).
• Tools and Libraries : Machine Learning Libraries, Version Control.
• Project Management : Problem Formulation, Project Planning.
  
  

Time period

• 6/6/2024 – 8/30/2024
• 3 days per week