Senior llm engineer

3 days ago

Hyderabad, India Altysys Full time

Experience : 7+ Years Relevant Experience : 4+ Years Work Mode : Hyderabad Budget : 2.6lpm Key Responsibilities: Model Expertise: Work with transformer models (GPT, BERT, T5, Ro BERTa, 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 (Co T) 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: 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, Ro BERTa, 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, Spa Cy, NLTK, Py Torch, Tensor Flow . Strong knowledge of model evaluation metrics (BLEU, ROUGE, perplexity, etc.). Experience in deploying models into production environments . Awareness of bias, hallucinations, and limitations in LLM outputs . Good to Have: Experience with LLM observability tools and monitoring pipelines. Exposure to cloud platforms (AWS, GCP, Azure) for scalable model deployment. Knowledge of MLOps practices for model lifecycle management.

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