FILPAL (S) Pte. Ltd. – AIML Software Engineer Intern

Job Summary:

Assist in the development and implementation of AI models focusing on Convolutional Neural Network (CNN)-based detection algorithms to enhance object detection and classification, and Reinforcement Learning (RL)-based algorithms for RF design optimization. The intern will work under supervision to integrate AI solutions into existing software frameworks, process RF data, and contribute to simulation and testing efforts. This role is ideal for a student from the Applied Artificial Intelligence course at SIT Singapore, providing hands-on experience in AI applications for real-world RF systems.

Key Responsibilities:

• Implement and fine-tune YOLO-based object detection models for analyzing RF signals and spectrograms.

• Collaborate with the development team to integrate AI models into Python-based back-end components, using libraries such as OpenCV, TensorFlow, or PyTorch.

• Assist in data preprocessing, including handling I/Q data, signal features, and simulation outputs.

• Participate in testing and validation of AI models using performance evaluation metrics like accuracy, precision, and recall.

• Participate in design optimization with reinforcement learning.

• Contribute to agile discussions, code reviews, and documentation of AI workflows.

• Explore enhancements to YOLO for real-time detection in noisy RF environments, such as adapting to multipath interference or signal fading.

• Support integration with existing RFDF tools, including databases (SQL/NoSQL) and visualization interfaces.

• Current enrollment in the Applied Artificial Intelligence course or equivalent AI-focused program.

• Basic understanding of machine learning concepts, with exposure to computer vision and decision making.

• Basic understanding of mathematical optimization algorithm such as GA, PSO, gradient descent or random search.

• Familiarity with programming in Python; experience with AI frameworks like PyTorch, or TensorFlow is desirable.

• Strong problem-solving skills and ability to learn independently in a technical environment.

• Good communication skills for team collaboration and reporting progress.

• Interest in RF systems, signal processing, or drone technology is a plus, but not mandatory.

Qualifications:

• Current enrollment in the Applied Artificial Intelligence course or equivalent AI-focused program.

• Basic understanding of machine learning concepts, with exposure to computer vision and decision making.

• Basic understanding of mathematical optimization algorithm such as GA, PSO, gradient descent or random search.

• Familiarity with programming in Python; experience with AI frameworks like PyTorch, or TensorFlow is desirable.

• Strong problem-solving skills and ability to learn independently in a technical environment.

• Good communication skills for team collaboration and reporting progress.

• Interest in RF systems, signal processing, or drone technology is a plus, but not mandatory.

Skillsets:

• Basic proficiency in Python programming and data handling (e.g., NumPy, Pandas).

• Understanding of object detection models, particularly YOLO variants.

• Familiarity with decision making RL algorithms is a plus.

• Familiarity with version control using Git is a plus.

• Ability to work with datasets for training and evaluating AI models.

• Strong analytical mindset for debugging and optimizing AI performance.

Optional Skillsets:

• Experience with signal processing libraries or RF simulation tools (Ansys HFSS, CST Studio, Keysight ADS).

• Knowledge of computer vision techniques for spectrogram analysis.

• Nvidia Jetson embedded system programming.

• Ability to prioritize tasks and manage time effectively in a project setting.

• Familiarity with agile methodologies or collaborative tools like GitLab.