Senior LLM Engineer

1 week ago

Greater Hyderabad Area, India Altysys Full time
Experience : 7+ Years

Relevant Experience : 4+ Years

Work Mode : Gurgaon

Budget : 2.6lpm

Key Responsibilities:

- 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:

- 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.
- 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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