NeGD is currently inviting applications for the following position on a purely contractual basis for an initial period of three years, which may be extended based on the requirements of the project.

Responsibilities

• Design and architect neural network models including Transformers, CNNs, RNNs, and hybrid architectures; make decisions on layer configurations, attention mechanisms, activation functions, and connectivity patterns for optimal performance
• Develop and implement training algorithms and optimization strategies including custom loss functions, learning rate schedules, gradient clipping, and regularization techniques to ensure stable convergence and generalization
• Fine-tune pre-trained foundation models (Llama, Mistral, BERT, GPT, T5) using Parameter- Efficient Fine-Tuning (PEFT) methods including LoRA, QLoRA, Prefix Tuning, and AdaLoRA for domain-specific applications
• Implement Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI methodologies; design reward models, policy optimization algorithms (PPO, DPO), and human preference learning systems
• Engineer high-quality training datasets through data collection strategies, cleaning pipelines, augmentation techniques, and synthetic data generation; ensure data representativeness and bias mitigation
• Design and execute comprehensive model evaluation frameworks including statistical significance testing, cross-validation strategies, benchmark dataset evaluation (MMLU, HumanEval, GLUE, SuperGLUE), and custom metrics development
• Develop Retrieval-Augmented Generation (RAG) architectures including embedding model selection, retrieval algorithms, context integration strategies, and relevance scoring mechanisms to enhance model accuracy
• Optimize model architectures for efficiency through knowledge distillation, model pruning, quantization-aware training, and neural architecture search (NAS) without compromising accuracy
• Perform rigorous statistical analysis and hypothesis testing on model outputs; identify failure modes, error analysis, and edge cases requiring architectural improvements
• Collaborate with domain experts to translate business requirements into mathematical formulations and ML problem statements; define target variables, feature spaces, and success criteria
• Mentor junior researchers and engineers on machine learning theory, algorithmic best practices, experimental design, and research methodologies; conduct code reviews for model implementations
• Document research findings, model architectures, training methodologies, and experimental results in technical reports; publish papers in conferences or journals and present at technical forums
• Analyze model interpretability and explainability using attention visualization, SHAP values, LIME, and gradient-based attribution methods to ensure transparency in AI decision-making

Important Links:

About National e-Governance Division (NeGD)

The National e-Governance Division (NeGD) is an independent business division under the Digital India Corporation, Ministry of Electronics and Information Technology. NeGD has been playing a pivotal role in supporting MeitY in Programme Management and implementation of e-Governance projects and initiatives undertaken by various Ministries/ Departments, both at the Central and State levels.

NeGD has been spearheading several innovative initiatives under the aegis of the Digital India Programme. Those have been developed keeping the vision areas of Digital India at the core- providing digital infrastructure as a core utility to every citizen, governance and services on demand and in particular, digital empowerment of the citizens of our country; some of these initiatives include DigiLocker, UMANG, Poshan Tracker, OpenForge Platform, API Setu, National Academic Depository, Academic Bank of Credits, and Learning Management System.