MBSE AI/ML Requirement-3 - ZR_2634_JOB

About the role

**Job Title:**

* MBSE AI/ML Requirement\-3 * **Experience:** * + 2 \- 4 Years * **Qualification:** * + B.Tech/B.E or M.Tech/M.E (ECE \& EEE)

* + 1 * **Job Location:** * + Chennai * **Skill Set:** * + Generative AI, LLM, RAG, Model Fine\-tuning, Deep\-Learning, Transformers (Please interview internally and Upload only pre\-vetted profiles) * **Experience:** * + 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. * **Skills:** * + 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. * **Job Description:** * + 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. * **Key Responsibilities:** * + 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.