Job Title: Deep Learning Engineer
Job Summary:
We are seeking a Fresher Deep Learning Engineer to assist in the development, training, and deployment of deep learning models for various AI applications. The ideal candidate should have a strong foundation in Python, deep learning architectures, and neural networks. This role involves working with large datasets, implementing deep learning models, and collaborating with data scientists and engineers to build intelligent AI-driven solutions.
Key Responsibilities:
- Assist in data collection, preprocessing, and augmentation for deep learning models
- Develop and train neural networks (CNNs, RNNs, LSTMs, Transformers) using TensorFlow and PyTorch
- Implement and experiment with deep learning models for tasks such as image classification, object detection, NLP, and speech recognition
- Optimize model performance and hyperparameters using techniques such as dropout, batch normalization, learning rate scheduling
- Work with transfer learning and pre-trained models (ResNet, BERT, GPT, YOLO) to fine-tune solutions
- Implement model evaluation metrics such as accuracy, precision, recall, F1-score, and confusion matrix analysis
- Deploy deep learning models using Flask, FastAPI, or cloud-based solutions (AWS, Google Cloud, Azure)
- Visualize and interpret model results using Matplotlib, Seaborn, and TensorBoard
- Collaborate with data engineers and software developers to integrate models into real-world applications
- Stay updated with latest deep learning research, tools, and frameworks
Skills and Knowledge Required:
- Strong Python programming skills
- Basic understanding of deep learning algorithms and neural network architectures
- Familiarity with deep learning frameworks (TensorFlow, PyTorch, Keras)
- Experience with data preprocessing and augmentation techniques
- Knowledge of activation functions, optimization algorithms (SGD, Adam), and loss functions
- Hands-on experience with NumPy, Pandas, OpenCV, and Scikit-learn
- Understanding of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms
- Basic knowledge of cloud computing and GPU acceleration (Google Colab, AWS EC2, NVIDIA CUDA, TensorRT) (optional)
- Exposure to model deployment and APIs (Flask, FastAPI, TensorFlow Serving) (a plus)
- Strong analytical and problem-solving skills
Educational Qualifications:
- Bachelor’s degree in Computer Science, AI, Data Science, Mathematics, or a related field
- Certifications in Deep Learning (Coursera, Udacity, Fast.ai, TensorFlow/PyTorch) are a plus
Experience:
- 0-1 year of experience in deep learning, neural networks, or AI-related projects
- Experience with academic projects, Kaggle competitions, or deep learning research papers preferred
Key Focus Areas:
- Neural Network Design & Training
- Deep Learning for Image Processing & NLP
- Model Optimization & Hyperparameter Tuning
- Deployment of Deep Learning Models
Tools and Technologies:
- Programming Languages: Python
- Deep Learning Frameworks: TensorFlow, PyTorch, Keras
- Data Processing & Visualization: NumPy, Pandas, OpenCV, Matplotlib, Seaborn
- Cloud & GPU Computing (Optional): AWS SageMaker, Google Colab, Azure ML, NVIDIA CUDA
- Deployment & APIs: Flask, FastAPI, TensorFlow Serving
- Version Control & MLOps: Git, MLflow, Docker (optional)
Other Requirements:
- Passion for AI and deep learning applications
- Ability to work independently and in a team environment
- Eagerness to explore and implement cutting-edge deep learning research
- Good communication and documentation skills