Senior Research Scientist – NLP, Foundation Models & Agentic AI

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 ● Cloud AI infrastructure

Preferred Qualifications Research Recognition Preference will be given to candidates who have received prestigious fellowships, awards, or recognitions such as: ● Prime Minister's Research Fellowship (PMRF) ● Google PhD Fellowship ● Microsoft Research Fellowship ● NVIDIA Fellowship ● JRF/SRF Research Fellowships ● National or International Research Awards ● Outstanding Thesis Awards Industry & Product Impact ● Experience building production-grade AI products. ● Proven track record of translating research into business impact. ● Experience leading AI initiatives from concept to deployment. ● Contributions to widely used AI platforms or products. OR Open Source & Community Contributions ● Significant GitHub contributions. ● Maintainer or contributor to major AI frameworks. ● Open-source model releases. ● Research toolkits and benchmark contributions. OR Intellectual Property ● Patents in AI, NLP, Deep Learning, or Generative AI. ● Technology transfer or commercialization experience.

What Will Make You Stand Out ● Publications in NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, or COLM. ● PMRF or equivalent prestigious fellowship. ● Experience training or contributing to foundation models with billions of parameters. ● Strong publication and citation record. ● Experience in Agentic AI and Reasoning Systems. ● Demonstrated ability to build and deploy advanced AI products. ● Open-source leadership and research community engagement. ● Ability to bridge scientific research and product innovation.