Information Technology
Full-Time
Ethics Infotech
Overview
Role & Responsibilities
- 4+ years of experience applying AI to practical uses.
- Develop and train computer vision models for tasks like:
- Object detection and tracking (YOLO, Faster R-CNN, etc.)
- Image classification, segmentation, OCR (e.g., PaddleOCR, Tesseract).
- Face recognition/blurring, anomaly detection, etc.
- Optimize models for performance on edge devices (e.g., NVIDIA Jetson, OpenVINO, TensorRT).
- Process and annotate image/video datasets; apply data augmentation techniques.
- Proficiency in Large Language Models.
- Strong understanding of statistical analysis and machine learning algorithms.
- Hands-on implementing various machine learning algorithms such as linear regression, logistic regression, decision trees, and clustering algorithms.
- Understanding of image processing concepts (thresholding, contour detection, transformations, etc.)
- Experience in model optimization, quantization, or deploying to edge (Jetson Nano/Xavier, Coral, etc.)
- Strong programming skills in Python (or C++), with expertise in:
- Implement and optimize machine learning pipelines and workflows for seamless integration into production systems.
- Hands-on experience with at least one real-time CV application (e.g., surveillance, retail analytics, industrial inspection, AR/VR).
- OpenCV, NumPy, PyTorch/TensorFlow.
- Computer vision models like YOLOv5/v8, Mask R-CNN, DeepSORT.
- Engage with multiple teams and contribute on key decisions.
- Expected to provide solutions to problems that apply across multiple teams.
- Lead the implementation of large language models in AI applications.
- Research and apply cutting-edge AI techniques to enhance system performance.
- Contribute to the development and deployment of AI solutions across various domains.
- Design, develop, and deploy ML models for:
- OCR-based text extraction from scanned documents (PDFs, images).
- Table and line-item detection in invoices, receipts, and forms.
- Named entity recognition (NER) and information classification.
- Evaluate and integrate third-party OCR tools (e.g., Tesseract, Google Vision API, AWS Textract, Azure OCR,PaddleOCR, EasyOCR).
- Develop pre-processing and post-processing pipelines for noisy image/text data.
- Familiarity with video analytics platforms (e.g., DeepStream, Streamlit-based dashboards).
- Experience with MLOps tools (MLflow, ONNX, Triton Inference Server).
- Background in academic CV research or published papers.
- Knowledge of GPU acceleration, CUDA, or hardware integration (cameras, sensors).
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