Senior LLM Engineer

Experience : 7+ Years

Relevant Experience : 4+ Years

Work Mode : Remote

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.