Senior LLM Engineer

Experience : 7+ Years

Relevant Experience : 4+ Years

Work Mode : Hyderabad

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.