Senior LLM Engineer

5 days ago

Bengaluru, Karnataka, India Vega Intellisoft Full time ₹ 8,00,000 - ₹ 18,00,000 per year

Designation: Senior LLM Engineer

Location: Bangalore

Budget: 8LPA-18LPA

Notice Period: Immediate

Roles and Responsibilities

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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