Senior LLM Engineer(Contract_Hyderabad)

4 days ago

Hyderabad, India Myliara Full time

Senior LLM Engineer

Experience : 7+years

Budget:150LPM

Onsite

Hyderabad

Contract:6 months

Immediate joiners preferred

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)

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