We are looking for a talented and driven Deep Learning Engineer to join our team. As a Deep Learning Engineer, you will be responsible for designing, developing, and implementing deep learning models to solve complex problems and advance our projects. Your expertise in deep learning algorithms and frameworks will be essential in pushing the boundaries of AI technology.
Job Responsibilities

• Model Development: Collaborate with the research and development team to understand project requirements and formulate deep learning solutions. Design, train, and optimize deep learning models for various applications.
• Data Preprocessing: Prepare and preprocess large datasets for model training and validation. Ensure data quality and integrity to achieve accurate and reliable model predictions.
• Environment Setup: Set up and maintain deep learning environments using frameworks like TensorFlow, PyTorch, or Keras. Utilize cloud-based platforms such as AWS, GCP, or Azure for scalability and efficiency.
• Model Training and Evaluation: Conduct experiments to train deep learning models and evaluate their performance using appropriate metrics. Fine-tune models to improve accuracy and efficiency.
• Deployment and Integration: Collaborate with software engineers to deploy deep learning models into production environments and integrate them with existing systems.
• Research and Innovation: Stay up-to-date with the latest advancements in deep learning research and apply cutting-edge techniques to improve our models and products.
• Documentation and Communication: Document your work thoroughly, including model architectures, training methodologies, and results. Present findings and progress to the team and stakeholders in a clear and concise manner.

• Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field.
• Solid understanding of deep learning algorithms and frameworks (e.g., TensorFlow, PyTorch, Keras).
• Proficiency in Python and its data science libraries (e.g., NumPy, Pandas).
• Experience with cloud-based platforms for model development and deployment (e.g., AWS, GCP, Azure).
• Familiarity with data preprocessing techniques and tools.
• Strong problem-solving skills and the ability to work effectively in a team environment.
• Excellent communication skills and the ability to explain complex concepts to non-technical stakeholders.

• Previous experience in deep learning projects, such as computer vision or natural language processing.
• Knowledge of GPU acceleration for deep learning training (e.g., CUDA).
• Familiarity with version control systems like Git.
• Understanding of big data technologies, such as Hadoop or Spark.