AI ML Engineer

· 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.) · 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). · 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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