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.

जिम्मेदारियां

• Architect self-hosted inference clusters using vLLM, TGI (Text Generation Inference), and TensorRT-LLM on on-premise NVIDIA DGX systems and GPU racks, ensuring sub-100ms latency for 70B+ parameter models
• Design parallel workflows on AWS SageMaker (Endpoints/Pipelines), Google Vertex AI (Prediction/Training), and Azure ML for elastic training workloads and managed foundation model APIs
• Implement cloud-agnostic model deployment using Kubernetes (EKS/GKE/AKS) with portability across private data centers and cloud VPCs, ensuring zero vendor lock-in
• Deploy multi-GPU inference parallelism (tensor + pipeline parallelism) for foundation models using Ray Serve, NVIDIA Triton, and custom FastAPI stacks
• Optimize inference economics through quantization (AWQ/GPTQ/FP8), KV-cache optimization, and continuous batching—reducing per-token costs by 40%+
• Build auto-scaling GPU node pools (Karpenter/Cluster Autoscaler) that respond to inference demand spikes within seconds
• Implement RLHF (Reinforcement Learning from Human Feedback) infrastructure using DeepSpeed, LoRA/QLoRA fine-tuning pipelines, and distributed training orchestration
• Design evaluation frameworks for LLMs: automated benchmarking (MMLU, HumanEval), A/B testing for model versions, and human-in-the-loop feedback systems
• Manage vector database infrastructure (Pinecone, Weaviate, Milvus, pgvector) for RAG systems spanning private and cloud environments
• Build CI/CD for ML using GitOps (ArgoCD/Flux) with model versioning (MLflow/DVC), automated testing for data drift, and canary deployments for model updates
• Implement feature stores (Feast/Tecton) and experiment tracking (Weights & Biases/MLflow) supporting both cloud and on-premise data lakes
• Create observability stacks for LLMs: token-level latency tracking, GPU memory saturation alerts, and cost-per-inference dashboards using Prometheus/Grafana/CloudWatch
• Manage secrets, model encryption at rest (HashiCorp Vault), and network policies (Istio/Linkerd) for multi-tenant model serving

 महत्वपूर्ण लिंक:

राष्ट्रीय ई-गवर्नेंस प्रभाग (NeGD) के बारे में

राष्ट्रीय ई-गवर्नेंस प्रभाग (एनईजीडी) डिजिटल इंडिया कॉर्पोरेशन, इलेक्ट्रॉनिक्स और सूचना प्रौद्योगिकी मंत्रालय के तहत एक स्वतंत्र व्यापार प्रभाग है। एनईजीडी केंद्र और राज्य दोनों स्तरों पर विभिन्न मंत्रालयों/विभागों द्वारा किए गए ई-गवर्नेंस परियोजनाओं और पहलों के कार्यक्रम प्रबंधन और कार्यान्वयन में एमईआईटीवाई का समर्थन करने में महत्वपूर्ण भूमिका निभा रहा है।

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.