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.