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Generative AI Engineer
2 weeks ago
We're Hiring: LLM Engineer
Location: Hyderabad – Onsite (5 Days a Week)
Duration: 6 Months (Contract Role)
Experience: 7 Years
Must Have Skill
● Overall 7+ years with at least 2+ years of experience working with transformer-based models and NLP tasks, with a focus on text generation, summarization, question answering, classification, and similar tasks.
● Expertise in transformer models like GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), T5 (Text-to-Text Transfer Transformer), RoBERTa, and similar models.
● Familiarity with model architectures, attention mechanisms, and self-attention layers that enable LLMs to generate human-like text.
● Experience in fine-tuning pre-trained models on domain-specific datasets for tasks such as text generation, summarization, question answering, classification, and translation.
● Familiarity with concepts like attention mechanisms, context windows, tokenization, and embedding layers.
● Awareness of biases, hallucinations, and knowledge cutoffs that can affect LLM performance and output quality.
● Expertise in crafting clear, concise, and contextually relevant prompts to guide LLMs towards generating desired outputs.
● Experience in instruction-based prompting
● Use of zero-shot, few-shot, and many-shot learning techniques for maximizing model performance without retraining.
● Experience in iterating on prompts to refine outputs, test model performance, and ensure consistent results.
● Crafting prompt templates for repetitive tasks, ensuring prompts are adaptable to different contexts and inputs.
● Expertise in chain-of-thought (CoT) prompting to guide LLMs through complex reasoning tasks by encouraging step-by-step breakdowns.
● Proficiency in Python and experience with NLP libraries (e.g., Hugging Face, SpaCy, NLTK).
● Experience with transformer-based models (e.g., GPT, BERT, T5) for text generation tasks.
● Experience in training, fine-tuning, and deploying machine learning models in an NLP context.
● Understanding of model evaluation metrics (e.g., BLEU, ROUGE
