AI Engineer - Computer Vision

AI Engineer - Computer Vision

Location: GIFT City, Gandhinagar, Gujarat - India. (Work from Office)

Experience: Internship (Full-Time)

About the Role

We are hiring an enthusiastic AI Engineer Intern with a strong interest in Computer Vision to join our team at GIFT City. You will get hands-on experience designing, training, optimizing, and deploying vision-based AI systems used across healthcare, fintech, manufacturing, retail automation, and enterprise applications.

This internship is ideal for learners who want real exposure to AI development, large-scale datasets, and production-grade model building for global use cases.

Key Responsibilities

• Develop, train, and optimize Computer Vision models for object detection, segmentation, OCR, anomaly detection, rPPG-based vitals extraction, and custom video analytics.
• Build robust data pipelines for image/video ingestion, cleaning, augmentation, and annotation workflows.
• Perform model evaluation using standard metrics such as mAP, IoU, F1, precision, recall, latency, and accuracy.
• Deploy models to production using TensorFlow, PyTorch, ONNX Runtime, or OpenVINO.
• Develop scalable inference APIs and integrate models with backend systems.
• Implement model optimization techniques including quantization, pruning, distillation, and GPU acceleration.
• Research and experiment with modern architectures like Transformers, YOLO variants, RNN/CNN hybrids, and lightweight mobile models.
• Collaborate with DevOps teams to containerize inference workloads using Docker and deploy on cloud platforms.
• Document experiments, results, and best practices clearly for internal teams and clients.

Required Skills

• Strong proficiency in Python and deep learning frameworks (PyTorch or TensorFlow).
• Solid understanding of machine learning algorithms and computer vision fundamentals.
• Experience with OpenCV, NumPy, and image/video preprocessing.
• Hands-on experience with datasets, annotations, and CV pipeline design.
• Ability to train, validate, and tune deep learning models.
• Familiarity with deploying ML workloads on cloud or containerized environments.
• Good analytical and debugging skills.

Nice-to-Have Skills

• Experience with YOLOv7/8, Detectron2, MediaPipe, OpenVINO, or ONNX Runtime.
• Exposure to rPPG signal extraction, vitals estimation, and video-based biometrics.
• Understanding of MLOps tools and practices.
• Knowledge of AWS, GCP, or Azure ML services.
• Experience with GPU optimization and real-time model performance tuning.

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