Generative AI Engineer

As a Generative AI Expert, your role will involve designing, developing, and optimizing Transformer-based models for various generative tasks such as text summarization, Q&A, content creation, and code generation. You will be required to implement deep learning techniques with a focus on batch normalization, dropout, residual connections, and attention mechanisms to enhance model training and convergence. Additionally, you will build and train contrastive learning frameworks for representation learning and fine-tuning pre-trained models on domain-specific data. Your responsibilities will also include developing scalable information retrieval systems and optimizing model performance for inference using various techniques. Key Responsibilities: - Design, develop, and optimize Transformer-based models like BERT, GPT, and T5 for generative tasks - Implement deep learning techniques including batch normalization, dropout, and attention mechanisms - Build and train contrastive learning frameworks for representation learning - Develop scalable information retrieval systems using technologies like FAISS - Optimize model performance for inference through techniques like quantization and distillation - Collaborate with data scientists and product teams to deliver user-focused GenAI solutions - Stay updated with the latest research in generative AI, deep learning, and retrieval-augmented generation (RAG) Qualifications Required: - Strong hands-on experience with Transformer architectures and frameworks like Hugging Face Transformers, PyTorch, or TensorFlow - Deep understanding of deep learning fundamentals and practical implementations - Experience with contrastive learning paradigms and self-supervised learning strategies - Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, JAX) - Knowledge of information retrieval systems, vector databases, and semantic search methodologies - Familiarity with MLOps tools and cloud platforms - Ability to handle large datasets and distributed training In addition, the job may require experience with Retrieval-Augmented Generation (RAG) pipelines, familiarity with fine-tuning methods for large language models, contributions to open-source GenAI projects, and experience with multi-modal models. Please note: No additional details of the company were present in the provided job description.,