AI/ML Model Development & OptimizationDesign, develop, and train deep learning models, including CNNs, RNNs, Transformers, and hybrid models for audio-video data fusion.Optimize models for performance, including reducing inference times and memory footprint for deployment on resource-constrained devices.
Data Collection & PreprocessingCollect, preprocess, and augment large-scale datasets, particularly for audio and video, ensuring high-quality input for model training.Implement data fusion techniques to combine multimodal data sources (audio, video) to create richer, more accurate models.
Edge Computing & DeploymentDeploy AI/ML models onto edge devices such as Raspberry Pi, NVIDIA Jetson, or Intel Movidius, ensuring real-time, efficient operation.Perform hardware-aware optimizations like quantization, pruning, and model compression to enable efficient edge deployment.
Real-time AI ApplicationsDevelop real-time AI applications using computer vision and deep learning techniques for tasks such as object detection, gesture recognition, and video analysis.Ensure high accuracy and low latency for real-time performance, both in cloud and on-device environments.
Collaboration with Cross-Functional TeamsCollaborate with software engineers, hardware engineers, and data scientists to integrate AI models into embedded systems, ensuring smooth deployment and functionality on edge devices.Work closely with product teams to translate business requirements into AI solutions, ensuring the models meet end-user needs.
Model Evaluation & MonitoringEvaluate the performance of deployed models using standard metrics (accuracy, precision, recall, F1 score) and real-world feedback to improve model performance over time.Continuously monitor and troubleshoot models post-deployment to ensure optimal performance and scalability.
Research & InnovationStay up-to-date with the latest advancements in AI/ML, computer vision, and edge computing, and explore new techniques to improve the efficiency and effectiveness of models.Experiment with novel architectures, including fusion models and multi-task learning, to solve complex problems in multimodal environments.
Desired Technical Skills:
Computer Vision & Image ProcessingStrong knowledge of object detection, image segmentation, feature extraction, and optical flow.Familiarity with tools like OpenCV, Dlib, and MediaPipe for real-time vision processing.Experience with GANs (Generative Adversarial Networks) for image generation or style transfer tasks.
Model ArchitecturesExpertise in designing and deploying CNNs, RNNs, Transformers, and hybrid models.In-depth understanding of model architectures for multimodal data fusion (e.g., combining audio-video inputs for predictive models).
Edge Computing & Embedded SystemsExperience deploying and optimizing models on edge devices (e.g., NVIDIA Jetson, Raspberry Pi, Intel Movidius).Familiarity with frameworks like TensorFlow Lite, OpenVINO, or TensorRT for model deployment on edge hardware.Ability to work with microcontrollers and edge AI platforms, optimizing performance for low-power and low-latency requirements.
Job Classification
Industry: IT Services & Consulting Functional Area / Department: Data Science & Analytics Role Category: Data Science & Machine Learning Role: Data Scientist Employement Type: Full time