Job Description
<p><p><b>Position : Senior LLM Engineer</b><br/><br/><b>Experience : Overall 7+Yrs</b><br/><br/><b>Relevant : 4+Yrs</b><br/><br/><b>Location : Hyderabad(Onsite)</b><br/><br/><b>Notice Period : Immediate Joiner</b><br/><br/><b>Key Responsibilities :</b></p><p><p><b><br/></b></p>- Model Expertise : Work with transformer models (GPT, BERT, T5, RoBERTa, etc.) across NLP tasks including text generation, summarization, classification, and translation.<br/><br/></p><p>- Model Fine-Tuning : Fine-tune pre-trained models on domain-specific datasets to optimize for summarization, text generation, question answering, and related tasks.<br/><br/></p><p>- Prompt Engineering : Design, test, and iterate on contextually relevant prompts to guide model outputs for desired performance.<br/><br/></p><p>- Instruction-Based Prompting : Implement and refine instruction-based prompting strategies to achieve contextually accurate results.<br/><br/></p><p>- Learning Approaches : Apply zero-shot, few-shot, and many-shot learning methods to maximize model performance without extensive retraining.<br/><br/></p><p>- Reasoning Enhancement : Leverage Chain-of-Thought (CoT) prompting for structured, step-by-step reasoning in complex tasks.<br/><br/></p><p>- Model Evaluation : Evaluate model performance using BLEU, ROUGE, and other relevant metrics; identify opportunities for improvement.<br/><br/></p><p>- Deployment : Deploy trained and fine-tuned models into production environments, </p><p>integrating with real-time systems and pipelines.</p><p><br/></p><p>- Bias & Reliability : Identify, monitor, and mitigate issues related to bias, hallucinations, and knowledge cutoffs in LLMs.<br/><br/></p><p>- Collaboration : Work closely with cross-functional teams (data scientists, engineers, product managers) to design scalable and efficient NLP-driven solutions.<br/><br/><b>Must-Have Skills :</b></p><p><p><b><br/></b></p>- 7+ years of overall experience in software/AI development with at least 2+ years in transformer-based NLP models.<br/><br/></p><p>- 4+ years of hands-on expertise with transformer architectures (GPT, BERT, T5, RoBERTa, etc.).<br/><br/></p><p>- Strong understanding of attention mechanisms, self-attention layers, tokenization, embeddings, and context windows.<br/><br/></p><p>- Proven experience in fine-tuning pre-trained models for NLP tasks (summarization, classification, text generation, translation, Q&A).<br/><br/></p><p>- Expertise in prompt engineering, including zero-shot, few-shot, many-shot learning, and </p><p>prompt template creation.<br/><br/></p><p>- Experience with instruction-based prompting and Chain-of-Thought prompting for reasoning tasks.<br/><br/></p><p>- Proficiency in Python and NLP libraries/frameworks such as Hugging Face Transformers, </p><p>SpaCy, NLTK, PyTorch, TensorFlow.<br/><br/></p><p>- Strong knowledge of model evaluation metrics (BLEU, ROUGE, perplexity, etc.).<br/><br/></p><p>- Experience in deploying models into production environments.<br/><br/></p><p>- Awareness of bias, hallucinations, and limitations in LLM outputs.<br/><br/><p><b>Good to Have :</b></p><p><br/></p>- Experience with LLM observability tools and monitoring pipelines.<br/><br/></p><p>- Exposure to cloud platforms (AWS, GCP, Azure) for scalable model deployment.<br/><br/></p><p>- Knowledge of MLOps practices for model lifecycle management.</p><br/></p> (ref:hirist.tech)