Senior LLM Engineer
3 days ago
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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