Senior Llm Engineer

6 days 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, Ro BERTa, 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 (Co T) 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, Ro BERTa, 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, Spa Cy, NLTK, Py Torch, Tensor Flow.

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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