Senior LLM Engineer
3 days ago
Profile Summary :
We are seeking a Senior LLM Engineer with deep expertise in transformer-based NLP models (GPT, BERT, T5, RoBERTa, etc.) and a strong command of prompt engineering, fine-tuning, and instruction-based learning. The ideal candidate will design, optimize, and deploy large language models (LLMs) for real-world applications in text generation, summarization, classification, and reasoning. This role requires a balance of research depth and engineering excellence, with hands-on experience in Python, Hugging Face Transformers, and deep learning frameworks such as PyTorch or TensorFlow.
Roles and Responsibilities :
- Develop, fine-tune, and optimize transformer models (GPT, BERT, T5, RoBERTa, etc.) for multiple NLP tasks (summarization, classification, translation, question answering).
- Perform domain-specific fine-tuning of pre-trained models to enhance contextual accuracy and performance.
- Design and iterate prompts and instruction-based strategies for guiding LLM behavior across use cases.
- Apply zero-shot, few-shot, and many-shot learning to improve model adaptability and performance.
- Implement Chain-of-Thought (CoT) prompting for improved reasoning and structured output.
- Evaluate models using BLEU, ROUGE, perplexity, and other key performance metrics.
- Deploy fine-tuned and optimized models into production pipelines with scalability and monitoring in mind.
- Identify, analyze, and mitigate biases, hallucinations, and factual inaccuracies in LLM outputs.
- Collaborate closely with data scientists, ML engineers, and product teams to deliver AI-driven solutions.
- Contribute to continuous improvement by staying updated with advances in LLM architectures, prompting methods, and evaluation techniques.
Mandatory Skills & Technical Proficiency :- Transformer-based models (GPT, BERT, T5, RoBERTa, etc.), attention mechanisms, embeddings, tokenization, context windows
- Fine-tuning, prompt engineering, zero/few/many-shot learning, instruction-based prompting, Chain-of-Thought reasoning
- Python, Hugging Face Transformers, PyTorch, TensorFlow, SpaCy, NLTK
- BLEU, ROUGE, perplexity, accuracy, F1-score
- Model deployment into production (APIs, real-time pipelines), monitoring for drift and reliability
- Bias detection, hallucination control, model interpretability
- Git, JIRA, CI/CD workflows for ML pipelines
Good to Have :
- Experience with LLM observability and monitoring tools.
- Exposure to cloud platforms (AWS, GCP, Azure) for scalable deployments.
- Understanding of MLOps principles for full model lifecycle management.
- Familiarity with vector databases, embedding stores, and retrieval-augmented generation (RAG).
Education :
- Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or related field, or equivalent practical experience.
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