Job Title:
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MBSE AI/ML Requirement-3
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Experience:
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2 - 4 Years
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Qualification:
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B.Tech/B.E or M.Tech/M.E (ECE & EEE)
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1
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Job Location:
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Chennai
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Skill Set:
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Generative AI, LLM, RAG, Model Fine-tuning, Deep-Learning, Transformers (Please interview internally and Upload only pre-vetted profiles)
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Experience:
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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.
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