Machine Learning Engineer

Role Overview:

We are seeking a Machine Learning Engineer with a strong background in AI/ML techniques and expertise in computer vision. The ideal candidate will have experience with model fine-tuning, working with pre-trained models, and deploying machine learning solutions at scale. You will be responsible for developing, optimizing, and fine-tuning ML models for various AI-powered applications, including image and video analysis, object detection, and more.

Key Responsibilities:

• Design, build, and fine-tune machine learning models for computer vision applications, such as object detection, segmentation, image classification, and scene understanding.
• Leverage pre-trained models (e.g., CNNs, transformers, YOLO, etc.) and perform fine-tuning to adapt them to specific use cases.
• Optimize model performance by implementing cutting-edge techniques in transfer learning, regularization, and hyperparameter tuning.
• Work closely with data scientists, engineers, and product teams to deliver AI solutions that meet business requirements.
• Develop scalable solutions and deployment pipelines to operationalize machine learning models.
• Maintain a strong focus on the efficiency of models, ensuring high speed and low latency in production environments.
• Stay up-to-date with the latest trends and technologies in AI, machine learning, and computer vision, applying this knowledge to improve existing solutions.
• Conduct rigorous evaluation of models using relevant metrics and implement strategies for performance improvement.
• Troubleshoot and resolve model-related issues in production environments.

Required Qualification

• s:Bachelor's/Master's/PhD in Computer Scien
• ce3+ years of experience in machine learning or deep learning, with a focus on computer visio
• n.Expertise in model fine-tuning, transfer learning, and working with pre-trained models (e.g., ResNet, VGG, EfficientNet, etc.
• ).Strong proficiency in AI/ML frameworks such as TensorFlow, PyTorch, Keras, or simila
• r.In-depth understanding of deep learning architectures (e.g., CNNs, RNNs, transformers
• ).Familiarity with vision-specific tasks such as image classification, object detection, semantic segmentation, and image generatio
• n.Hands-on experience with training large models and optimizing them for performance and resource constraint
• s.Knowledge of data preprocessing, augmentation, and labeling techniques for vision task
• s.Proficiency in programming languages such as Python, C++, or simila
• r.Strong understanding of model evaluation techniques and metrics for computer vision tasks (e.g., mAP, IoU, AUC
• ).Experience with cloud platforms (AWS, GCP, Azure) and containerization technologies like Docker is a plu