Job Overview:
We are seeking a highly skilled Deep Learning Engineer to join our AI/ML team. The ideal candidate will be responsible for designing, developing, and deploying deep learning models to solve complex real-world problems. You will work with large datasets, optimize neural networks, and collaborate with cross-functional teams to bring AI-powered solutions to production.
Key Responsibilities:
- Design, implement, and optimize deep learning models for computer vision, NLP, speech recognition, or other AI applications.
- Preprocess and analyze large-scale datasets to ensure high-quality model training.
- Fine-tune pre-trained models and experiment with state-of-the-art architectures to improve performance.
- Utilize frameworks like TensorFlow, PyTorch, or JAX for deep learning development.
- Optimize models for deployment on cloud platforms (AWS, GCP, Azure) or edge devices.
- Implement MLOps best practices for model versioning, monitoring, and deployment.
- Collaborate with data scientists, software engineers, and domain experts to integrate models into real-world applications.
- Stay updated with the latest advancements in AI, deep learning, and machine learning research.
Required Skills & Qualifications:
- Bachelor’s, Master’s, or Ph.D. in Computer Science, Artificial Intelligence, Data Science, or a related field.
- Strong proficiency in Python and deep learning frameworks (TensorFlow, PyTorch, Keras).
- Experience in CNNs, RNNs, Transformers, GANs, and other deep learning architectures.
- Solid understanding of data preprocessing, augmentation, and feature engineering.
- Familiarity with cloud platforms (AWS, GCP, Azure) and model deployment techniques (ONNX, TensorRT, TFLite).
- Experience with GPU acceleration (CUDA, cuDNN) for model training and optimization.
- Strong problem-solving skills and ability to optimize hyperparameters for improved model performance.
- Knowledge of big data processing tools (Spark, Hadoop) and SQL/NoSQL databases is a plus.
- Excellent communication and teamwork skills to collaborate effectively.
Preferred Qualifications:
Publications in top AI/ML conferences (NeurIPS, CVPR, ICML, ICLR) are a plus.
Experience with MLOps tools (Docker, Kubernetes, MLflow, Airflow).
Knowledge of reinforcement learning, generative models, and self-supervised learning.
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