AI Engineer for Large Language Models
6 days ago
As a senior LLM engineer, you will be working with transformer models across various NLP tasks. Your primary responsibility will be to fine-tune pre-trained models on domain-specific datasets to optimize for summarization, text generation, question answering, and related tasks.
Key responsibilities include designing, testing, and iterating on contextually relevant prompts to guide model outputs for desired performance. You will also implement and refine instruction-based prompting strategies to achieve contextually accurate results.
Additionally, you will apply zero-shot, few-shot, and many-shot learning methods to maximize model performance without extensive retraining. Chain-of-Thought (CoT) prompting will be used for structured, step-by-step reasoning in complex tasks. Model performance will be evaluated using BLEU, ROUGE, and other relevant metrics; identifying opportunities for improvement.
Lastly, you will deploy trained and fine-tuned models into production environments, integrating with real-time systems and pipelines. This includes identifying, monitoring, and mitigating issues related to bias, hallucinations, and knowledge cutoffs in LLMs.
Required Skills and QualificationsMust-Have Skills:
- 7+ years of overall experience in software/AI development with at least 2+ years in transformer-based NLP models.
- 4+ years of hands-on expertise with transformer architectures (GPT, BERT, T5, RoBERTa, etc.).
- Strong understanding of attention mechanisms, self-attention layers, tokenization, embeddings, and context windows.
- Proven experience in fine-tuning pre-trained models for NLP tasks (summarization, classification, text generation, translation, Q&A).
- Expertise in prompt engineering, including zero-shot, few-shot, many-shot learning, and prompt template creation.
- Experience with instruction-based prompting and Chain-of-Thought prompting for reasoning tasks.
- Proficiency in Python and NLP libraries/frameworks such as Hugging Face Transformers, SpaCy, NLTK, PyTorch, TensorFlow.
- Strong knowledge of model evaluation metrics (BLEU, ROUGE, perplexity, etc.).
- Experience in deploying models into production environments.
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