Senior Research Scientist NLP, Foundation Models & Agentic AI (Secunderabad)

About the Role We are seeking a world-class Research Scientist to lead cutting-edge research and development in Natural Language Processing (NLP), Foundation Models, Generative AI, Reasoning Systems, Agentic AI, and Multimodal Intelligence. The ideal candidate combines strong academic research credentials with hands-on experience building and deploying advanced AI systems. This role requires deep expertise in modern machine learning, large-scale model development, scientific experimentation, and translating research breakthroughs into impactful products and platforms. The candidate will work at the intersection of fundamental research and applied AI innovation, contributing to next-generation intelligent systems that can reason, plan, learn, retrieve knowledge, and interact autonomously across multiple modalities. Key Responsibilities Research & Innovation 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 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 Continuous improvement Evaluation & Benchmarking Develop robust evaluation methodologies for: Reasoning Hallucination Reduction Agent Performance Retrieval Quality Safety 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 Education PhD or M.Tech/MS in Computer Science, Artificial Intelligence, Machine Learning, NLP, Data Science, Computational Linguistics, or related fields. Candidates from premier institutions such as IITs, IISc, IIITs, top international universities, or equivalent research institutions are strongly preferred. Research Publications Must have a proven publication record in leading AI/ML/NLP conferences and journals, including but not limited to: NeurIPS ICML ICLR ACL EMNLP NAACL COLM Equivalent top-tier international conferences and journals Preferred: First-author publications Highly cited publications Best paper nominations or awards Core Technical Skills Machine Learning & Deep Learning Advanced Machine Learning Deep Learning Representation Learning Self-Supervised Learning Transfer Learning Optimization Techniques Statistical Learning Theory NLP & LLMs Transformers Attention Mechanisms Encoder-Decoder Architectures Foundation Models Large Language Models Instruction Tuning Prompt Engineering Long-Context Architectures Mixture of Experts (MoE) Parameter Efficient Fine-Tuning (PEFT) Reasoning & Agentic AI Chain-of-Thought Reasoning Test-Time Compute Optimization Reflection & Self-Correction Agent Frameworks Multi-Agent Systems Planning and Tool Usage Autonomous Decision-Making Systems Workflow Orchestration Retrieval & Knowledge Systems Retrieval-Augmented Generation (RAG) Dense Retrieval Hybrid Search Knowledge Graphs Semantic Search Vector Databases Memory Systems Knowledge Grounding Reinforcement Learning RLHF RLAIF DPO GRPO Reward Modeling Preference Optimization Multimodal AI Vision-Language Models Image Understanding Audio Understanding Video Understanding Multimodal Retrieval Cross-Modal Learning Programming & Engineering Skills Python (Expert) PyTorch (Expert) TensorFlow/JAX (Preferred) Kubernetes Docker Linux AI Infrastructure Experience Strong hands-on experience in: Training large-scale models Multi-GPU environments Distributed systems Model optimization Inference acceleration GPU utilization optimization Production AI systems MLOps platforms