Senior Research Scientist - NLP, Foundation Models & Agentic AI

As a Research Scientist at our company, you will lead cutting-edge research and development in Natural Language Processing (NLP), Foundation Models, Generative AI, Reasoning Systems, Agentic AI, and Multimodal Intelligence. Your role will involve conducting original research, designing novel architectures and algorithms, and contributing to next-generation intelligent systems that can reason, plan, learn, retrieve knowledge, and interact autonomously across multiple modalities. Key Responsibilities: - Conduct original research in NLP, Deep Learning, Generative AI, Foundation Models, Agentic AI, and Multimodal AI. - Design and develop novel architectures, algorithms, and training methodologies for large-scale AI systems. - Investigate emerging areas such as Reasoning Models, Agentic Workflows, Multi-Agent Systems, Long-Context LLMs, Retrieval-Augmented Generation (RAG), Memory-Augmented Systems, AI Alignment & Safety, Synthetic Data Generation, Knowledge Grounding, and Continual Learning. Foundation Model Development: - Design, train, fine-tune, and evaluate large language models and foundation models. - Develop efficient training and inference methodologies. - Work on instruction tuning, alignment, preference optimization, and reinforcement learning-based approaches. - Build scalable model pipelines for experimentation and deployment. AI Product Development: - Translate research innovations into deployable AI capabilities. - Collaborate with engineering and product teams to productionize research outcomes. - Design end-to-end AI solutions covering Data collection, Data curation, Model training, Evaluation, Deployment, Monitoring, and Continuous improvement. Evaluation & Benchmarking: - Develop robust evaluation methodologies for Reasoning, Hallucination Reduction, Agent Performance, Retrieval Quality, Safety, and User Experience. - Design benchmarks and experimental frameworks for model comparison and validation. Leadership & Collaboration: - Mentor junior researchers and ML engineers. - Drive technical strategy for advanced AI initiatives. - Publish research findings in leading conferences and journals. - Represent the organization in academic, research, and industry forums. Required Qualifications: - PhD or M.Tech/MS in Computer Science, Artificial Intelligence, Machine Learning, NLP, Data Science, Computational Linguistics, or related fields. - Proven publication record in leading AI/ML/NLP conferences and journals. - Strong technical skills in Machine Learning, Deep Learning, NLP, LLMs, Reasoning, Agentic AI, Retrieval & Knowledge Systems, Reinforcement Learning, Multimodal AI, and Programming & Engineering. By bringing your expertise and experience to our team, you will have the opportunity to contribute to groundbreaking research and shape the future of AI technology. As a Research Scientist at our company, you will lead cutting-edge research and development in Natural Language Processing (NLP), Foundation Models, Generative AI, Reasoning Systems, Agentic AI, and Multimodal Intelligence. Your role will involve conducting original research, designing novel architectures and algorithms, and contributing to next-generation intelligent systems that can reason, plan, learn, retrieve knowledge, and interact autonomously across multiple modalities. Key Responsibilities: - Conduct original research in NLP, Deep Learning, Generative AI, Foundation Models, Agentic AI, and Multimodal AI. - Design and develop novel architectures, algorithms, and training methodologies for large-scale AI systems. - Investigate emerging areas such as Reasoning Models, Agentic Workflows, Multi-Agent Systems, Long-Context LLMs, Retrieval-Augmented Generation (RAG), Memory-Augmented Systems, AI Alignment & Safety, Synthetic Data Generation, Knowledge Grounding, and Continual Learning. Foundation Model Development: - Design, train, fine-tune, and evaluate large language models and foundation models. - Develop efficient training and inference methodologies. - Work on instruction tuning, alignment, preference optimization, and reinforcement learning-based approaches. - Build scalable model pipelines for experimentation and deployment. AI Product Development: - Translate research innovations into deployable AI capabilities. - Collaborate with engineering and product teams to productionize research outcomes. - Design end-to-end AI solutions covering Data collection, Data curation, Model training, Evaluation, Deployment, Monitoring, and Continuous improvement. Evaluation & Benchmarking: - Develop robust evaluation methodologies for Reasoning, Hallucination Reduction, Agent Performance, Retrieval Quality, Safety, and User Experience. - Design benchmarks and experimental frameworks for model comparison and validation. Leadership & Collaboration: - Mentor junior researchers and ML engineers. - Drive technical strategy for advanced AI initiatives. - Publish research findings in leading con