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 product ionize 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

●…