NeGD is currently inviting applications for the AI/ML Engineer position on a contractual basis, initially for three years, with possible extensions based on project requirements.

Responsibilities

Model Development & Training
• Develop, train and fine-tune machine learning and deep learning models for use cases such as text classification, summarisation, entity extraction, document understanding, image matching, object detection and speech recognition
• Select architectures and pre-trained models suited to the task, the data and the deployment constraints, starting from strong baselines and preferring transfer learning to training from scratch
• Build training data pipelines covering preprocessing, feature engineering, data augmentation and synthetic data where appropriate, with versioned datasets
• Run reproducible, tracked experiments, including hyperparameter tuning, and perform systematic error analysis to improve accuracy, robustness and latency

LLM Adaptation & Applications
• Fine-tune open-weight LLMs and embedding models through supervised fine-tuning, parameter-efficient methods (LoRA, QLoRA), preference optimisation (DPO) and
reinforcement learning with verifiable rewards (GRPO and its variants), where these outperform prompting
• Distil larger models into smaller, cost-efficient models suitable for production
• Build retrieval-augmented and agentic systems with sound retrieval design, structured outputs and tool calling, integrated across commercial, cloud and self-hosted model providers through an abstraction layer
• Engineer the context supplied to models (retrieved content, memory, tool outputs and instructions), and set reasoning and token budgets for reasoning models against cost and latency targets
• Choose between prompting, retrieval, fine-tuning and smaller specialised models based on accuracy, cost, latency and data-governance requirements, and document the rationale

Computer Vision, Document & Speech AI
• Develop document-understanding pipelines combining OCR, layout analysis and vision language models for forms, scanned documents and handwritten records
• Develop and fine-tune vision models for classification, detection, segmentation and image similarity, including embedding-based matching
• Develop and fine-tune speech recognition and synthesis models, including for Indian languages and code-mixed speech

Inference Optimisation & Serving
• Optimise models through quantisation (including FP8 and FP4 formats), pruning, distillation and compilation (ONNX, TensorRT), and apply serving optimisations such as continuous batching, prefix caching and speculative decoding
• Benchmark accuracy, latency, throughput, memory and cost per request on CPU and GPU against defined service-level targets
• Deploy models with production serving frameworks, including autoscaling, versioning and rollback, in coordination with the MLOps team
• Monitor deployed models for accuracy drift, latency, GPU utilisation and cost, and feed production failures back into training and evaluation data

Evaluation & Quality
• Define task-appropriate metrics (such as F1, AUC, BLEU, chrF, ROUGE, WER, CER and mAP), and build golden datasets, benchmarks and regression suites that run as release gates
• Evaluate LLM and retrieval systems for groundedness, hallucination and retrieval quality, using LLM-based evaluation calibrated against human labels
• Evaluate agentic systems on trajectories, tool-call correctness and task completion, and diagnose failures from execution traces
• Conduct subgroup analysis across languages, demographics, document types and acoustic conditions, and work with QA on acceptance thresholds

Responsible AI, Safety & Privacy
• Apply guardrails and test models for prompt injection, jailbreaks, leakage of personal data and unsafe outputs, in coordination with AI safety specialists
• Measure and mitigate performance disparities across cohorts, and document models through model cards, dataset documentation and evaluation records
• Apply anonymisation and data minimisation in training and inference in compliance with applicable data protection law, and verify the licences of models and datasets used

Collaboration & Mentoring
• Work with data scientists, data engineers, MLOps and application teams to integrate models into production applications through well-defined APIs
• Critically assess emerging models and research for practical value, review the work of junior engineers, and review model deliverables from external vendors

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, Learning Management System.