Design, develop, fine-tune, and optimize generative deep learning models (GANs, VAEs, transformers). Collaborate with cross-functional teams to integrate and deploy AI solutions, troubleshoot model issues, document models, and communicate technical concepts to non-technical stakeholders.
Key Responsibilities:
- Model Development: Design and develop algorithms for generative models using deep learning techniques.
- Collaboration: Work with cross-functional teams to integrate generative AI solutions into existing systems.
- Research: Stay updated on the latest advancements in generative AI technologies and methodologies.
- Optimization: Fine-tune models for performance and efficiency.
- Troubleshooting: Address and resolve issues related to generative AI models and implementations.
- Documentation: Create and maintain comprehensive documentation for AI models and their applications.
- Communication: Explain complex technical concepts to non-technical stakeholders.
Required Skills and Qualifications:
- 6+ years of strong background in machine learning and deep learning algorithms.
- Proficiency in programming languages such as Python, with experience in frameworks like TensorFlow and PyTorch.
- Familiarity with natural language processing (NLP) techniques and transformer models (e.g., GPT, BERT)
- Hands on experience with prompt structures and fine-tune model outputs to align with business needs and user expectations.
- Experience with generative AI techniques, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
- Skills in data preprocessing and feature engineering for AI model training.
- Strong understanding of neural network architectures and optimization techniques.
- Experience in deploying AI models into production environments.
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