Job Summary

As a Generative AI Engineer specialized in Large Language Models (LLMs) you will work on developing implementing and optimizing state-of-the-art generative models that tackle a wide range of complex challenges in AI. You will be part of a multidisciplinary team pushing the frontiers of language understanding and generation contributing to the research development and deployment of large-scale AI systems that can generate coherent contextually aware and human-like text.

Responsibilities

Proven experience in developing and working with state-of-the-art large language models (LLMs) such as GPT BERT T5 or other transformer-based models.

Strong expertise in training fine-tuning and optimizing LLMs for real-world applications.

Hands-on experience with ML frameworks like TensorFlow PyTorch Hugging Face Transformers etc.

Experience with advanced techniques in NLP such as attention mechanisms transfer learning and few-shot learning.

Practical knowledge of deploying AI models at scale in production environments.

Expertise in deep learning and machine learning algorithms particularly in the context of generative models.

Proficiency in programming languages like Python and familiarity with libraries such as NumPy Pandas Scikit-learn and others.

Familiarity with cloud-based solutions and tools (AWS GCP or Azure) for scalable model training and deployment.

Knowledge of distributed computing and parallelism for large-scale training.

Stay updated with the latest technological advancements to incorporate into ongoing projects.

Contribute to the continuous improvement of development processes and methodologies.

Qualifications

Research & Development: Lead and contribute to research initiatives focused on generative models particularly LLMs like GPT BERT T5 and cutting-edge transformer architectures.

Model Design & Implementation: Design implement and optimize LLM architectures for text generation completion summarization and other NLP tasks.

Training & Fine-Tuning: Conduct training and fine-tuning of large-scale models on specialized datasets leveraging modern machine learning frameworks such as TensorFlow PyTorch and Hugging Face Transformers.

Optimization: Work on scaling optimizing and improving the efficiency of large models including distributed training parallelism and hardware acceleration (GPUs TPUs).

Deployment & Integration: Collaborate with engineering teams to integrate generative models into production systems and applications ensuring the scalability robustness and efficiency of deployed models.

Certifications Required

Generative AI certifications.

Sr.Developer