Overview

Design and develop deep learning models for tasks such as image classification, object detection, speech recognition, and natural language processing.

Responsibilities

• Train, evaluate, and optimize neural networks using large-scale datasets and advanced techniques like transfer learning, data augmentation, and hyperparameter tuning.
• Implement state-of-the-art deep learning architectures, including CNNs, RNNs, LSTMs, Transformers, GANs, and autoencoders.
• Collaborate with cross-functional teams including data scientists, software engineers, and product managers to define and deliver AI-powered features and solutions.
• Build and maintain scalable data pipelines for preprocessing, labeling, and augmentation of structured and unstructured data.
• Conduct research and literature reviews to evaluate and implement the latest algorithms and advancements in deep learning.
• Optimize model performance for deployment using techniques like quantization, pruning, and model compression.
• Deploy models into production environments, ensuring performance, reliability, and scalability using cloud platforms (AWS, GCP, Azure) or on-edge devices.
• Write clean, maintainable, and well-documented code that adheres to software engineering best practices.
• Monitor and troubleshoot deployed models, tracking key performance metrics and retraining when necessary.
• Ensure ethical AI practices, including fairness, explainability, and accountability in model development and deployment.
• Stay up-to-date with the AI/ML community, contributing to internal knowledge-sharing sessions and possibly publishing research papers or blog posts.