Senior LLM Engineer

Model Expertise:

Work with transformer models (GPT, BERT, T5, RoBERTa, etc.) across NLP tasks including text generation, summarization, classification, and translation.
Model Fine-Tuning: Fine-tune pre-trained models on domain-specific datasets to optimize for summarization, text generation, question answering, and related tasks.

Prompt Engineering:

Design, test, and iterate on contextually relevant prompts to guide model outputs for desired performance.
Instruction-Based Prompting: Implement and refine instruction-based prompting strategies to achieve contextually accurate results.

Learning Approaches:

Apply zero-shot, few-shot, and many-shot learning methods to maximize model performance without extensive retraining.
Reasoning Enhancement: Leverage Chain-of-Thought (CoT) prompting for structured, step-by-step reasoning in complex tasks.Model Evaluation: Evaluate model performance using BLEU, ROUGE, and other relevant metrics; identify opportunities for improvement.Deployment: Deploy trained and fine-tuned models into production environments, integrating with real-time systems and pipelines.Bias & Reliability: Identify, monitor, and mitigate issues related to bias, hallucinations, and knowledge cutoffs in LLMs.

Collaboration:

Work closely with cross-functional teams (data scientists, engineers, product managers) to design scalable and efficient NLP-driven solutions.

Must-Have Skills: