Machine Learning & Artifial Intelligence Internship

About the Role

We are looking for an ML / AI Intern interested in building practical AI systems. You will work with our engineering team on real-world projects involving areas such as computer vision, machine learning, language models, data processing, and deployment. We are looking for someone with good fundamentals, curiosity, and the ability to learn and build.

What You Will Work On

• Model Development: Design, train and fine-tune deep learning models for images, text and audio using frameworks like Pytorch and Tensorflow.
• Computer Vision: Work on image processing, classification, object detection, tracking, or similar vision tasks depending on project requirements.
• Data Pipelines: Curate, clean, annotate and preprocess multimodal datasets to ensure high-quality inputs for training and evaluation.
• Model Optimization: Compress and optimize models for edge deployment using quantization, pruning and conversion to TensorRT / TFLite / ONNX.
• Model Integration: Develop Python scripts to integrate models with real-time video streams, text APIs, voice inferences and other sensor data where relevant.
• Benchmarking: Measure FPS, accuracy, latency and thermal behavior on target hardware and iterate on models to improve efficiency.
• Documentation: Record experiments, code, and results in a clear, concise format and stay updated with relevant AI/ML techniques.

What We're Looking For

• Python: Comfortable writing Python code and familiar with basic data structures, functions, modules, and debugging.
• ML Fundamentals: Basic understanding of machine learning concepts such as training, validation, overfitting, evaluation metrics, and common model architectures.
• Deep Learning Frameworks: Some hands-on exposure to PyTorch, TensorFlow, or similar frameworks through coursework, projects, or self-learning.
• Computer Vision: Basic familiarity with image processing or computer vision concepts using OpenCV or similar tools.
• Development Tools: Basic familiarity with Git/GitHub and willingness to work with Linux and command-line tools.
• Learning Ability: Comfortable learning unfamiliar tools, libraries, and concepts while working on practical engineering problems.

Nice-to-Have

• Computer Vision Models: Exposure to YOLO, ResNet, MobileNet, object detection, tracking, pose estimation, or similar models.
• NLP / LLMs: Experience with embeddings, transformers, semantic search, text classification, information extraction, or chatbots.
• Edge Hardware: Projects involving Raspberry Pi, NVIDIA Jetson, etc.
• Deployment: Familiarity with ONNX Runtime, TensorRT, TensorFlow Lite, etc.
• DevOps: Basic understanding of Docker or containerized applications.
• Projects: Personal, academic, hackathon, or freelance projects related to AI/ML, preferably available through GitHub, a portfolio, or project documentation.

What You Will Gain

• Real-World AI Experience: Work on AI systems intended for practical business and engineering applications.
• End-to-End Exposure: Understand the workflow from data preparation and model development to integration and deployment.
• Breadth in AI: Gain exposure to computer vision, language models, multimodal AI, and edge deployment.
• Production Mindset: Learn about latency, reliability, accuracy, model optimization, and maintaining AI systems beyond experimentation.
• Industry Exposure: Understand how AI systems interact with hardware, automation, safety, and real operational environments.