Machine Learning Engineer

Role : ML Engineer Job Type: In-office (Hyderabad)

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 Qualifications:

● Bachelor's/Master's/PhD in Computer Science

● 3+ years of experience in machine learning or deep learning, with a focus on computer vision.

● 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 similar.

● 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 generation.

● Hands-on experience with training large models and optimizing them for performance and resource constraints.

● Knowledge of data preprocessing, augmentation, and labeling techniques for vision tasks.

● Proficiency in programming languages such as Python, C++, or similar.

● 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 plus.