3 Nvidia Gpus Jobs

Description and Requirements Key Responsibilities : Lead end-to-end transitions of AI PoCs into production environments, managing the entire process from testing to final deployment. Configure, install, and validate AI systems using key platforms, including VMware ESXi and vSphere for server virtualization, Linux (Ubuntu/RHEL) and Windows Server for operating system integration, Docker and Kubernetes for containerization and orchestration of AI workloads. Conduct comprehensive performance benchmarking and AI inferencing tests to validate system performance in production. Optimize deployed AI models for accuracy, performance, and scalability to ensure they meet production-level requirements and customer expectations. Serve as the primary technical lead/SME for the AI POC deployment in enterprise environments, focusing on AI solutions powered by Nvidia GPUs. Work hands-on with Nvidia AI Enterprise and GPU-accelerated workloads, ensuring efficient deployment and model performance using frameworks such as PyTorch and TensorFlow. Lead technical optimizations aimed at resource efficiency, ensuring that models are deployed effectively within the customer's infrastructure. Ensure the readiness of customer environments to handle, maintain, and scale AI solutions post-deployment. take ownership of AI project deployments, overseeing all phases from planning to final deployment, ensuring that timelines and deliverables are met. Collaborate with stakeholders, including cross-functional teams (e.g., Lenovo AI Application, solution architects), customers, and internal resources to coordinate deployments and deliver results on schedule. Implement risk management strategies and develop contingency plans to mitigate potential issues such as hardware failures, network bottlenecks, and software incompatibilities. Maintain ongoing, transparent communication with all relevant stakeholders, providing updates on project status and addressing any issues or changes in scope. Experience : Overall experience 7-10 years Relevant experience of 2-4 years in deploying AI/ML models/ AI solutions using Nvidia GPUs in enterprise production environments. Demonstrated success in leading and managing complex AI infrastructure projects, including PoC transitions to production at scale. Technical Expertise: Experience in the area of Retrieval Augmented Generation (RAG), NVIDIA AI Enterprise, NVIDIA Inference Microservices (NIMs), Model Management, Kubernetes Extensive experience with Nvidia AI Enterprise, GPU-accelerated workloads, and AI/ML frameworks such as PyTorch and TensorFlow. Proficient in deploying AI solutions across enterprise platforms, including VMware ESXi, Docker, Kubernetes, and Linux (Ubuntu/RHEL) and Windows Server environments. MLOps proficiency with hands-on experience using tools such as Kubeflow, MLflow, or AWS SageMaker for managing the AI model lifecycle in production. Strong understanding of virtualization and containerization technologies to ensure robust and scalable deployments.

Dharmakit Networks is a premium global IT solutions partner dedicated to innovation and success worldwide. Specializing in website development, SaaS, digital marketing, AI Solutions, and more, we help brands turn their ideas into high-impact digital products. Known for blending global standards with deep Indian insight, we are now stepping into our most exciting chapter yet. Project Ax1 is our next-generation Large Language Model (LLM), a powerful AI initiative designed to make intelligence accessible and impactful for Bharat and the world. Built by a team of AI experts, Dharmakit Networks is committed to developing cost-effective, high-performance AI tailored for India and beyond, enabling enterprises to unlock new opportunities and drive deeper connections. Join us in reshaping the future of AI, starting from India. As a GPU Infrastructure Engineer, you will be at the core of building, optimizing, and scaling the GPU and AI compute infrastructure that powers Project Ax1. Your responsibilities will include designing, deploying, and optimizing GPU infrastructure for large-scale AI workloads, managing GPU clusters across cloud (AWS, Azure, GCP) and on-prem setups, setting up and maintaining model CI/CD pipelines for efficient training and deployment, optimizing LLM inference using TensorRT, ONNX, Nvidia NVCF, and more. You will also be responsible for managing offline/edge deployments of AI models, building and tuning data pipelines to support real-time and batch processing, monitoring model and infra performance for availability, latency, and cost efficiency, and implementing logging, monitoring, and alerting using tools like Prometheus, Grafana, ELK, CloudWatch. Collaboration with AI Experts, ML Experts, backend Experts, and full-stack teams will be essential to ensure seamless model delivery. **Key Responsibilities:** - Design, deploy, and optimize GPU infrastructure for large-scale AI workloads. - Manage GPU clusters across cloud (AWS, Azure, GCP) and on-prem setups. - Set up and maintain model CI/CD pipelines for efficient training and deployment. - Optimize LLM inference using TensorRT, ONNX, Nvidia NVCF, etc. - Manage offline/edge deployments of AI models (e.g., CUDA, Lambda, containerized AI). - Build and tune data pipelines to support real-time and batch processing. - Monitor model and infra performance for availability, latency, and cost efficiency. - Implement logging, monitoring, and alerting using Prometheus, Grafana, ELK, CloudWatch. - Work closely with AI Experts, ML Experts, backend Experts, and full-stack teams to ensure seamless model delivery. **Must-Have Skills And Qualifications:** - Bachelors degree in Computer Science, Engineering, or related field. - Hands-on experience with Nvidia GPUs, CUDA, and deep learning model deployment. - Strong experience with AWS, Azure, or GCP GPU instance setup and scaling. - Proficiency in model CI/CD and automated ML workflows. - Experience with Terraform, Kubernetes, and Docker. - Familiarity with offline/edge AI, including quantization and optimization. - Logging & Monitoring using tools like Prometheus, Grafana, CloudWatch. - Experience with backend APIs, data processing workflows, and ML pipelines. - Experience with Git, collaboration in agile, cross-functional teams. - Strong analytical and debugging skills. - Excellent communication, teamwork, and problem-solving abilities. **Good To Have:** - Experience with Nvidia NVCF, DeepSpeed, vLLM, Hugging Face Triton. - Knowledge of FP16/INT8 quantization, pruning, and other optimization tricks. - Exposure to serverless AI inference (Lambda, SageMaker, Azure ML). - Contributions to open-source AI infrastructure projects or a strong GitHub portfolio showcasing ML model deployment expertise.,

Yubi, formerly known as CredAvenue, is re-defining global debt markets by freeing the flow of finance between borrowers, lenders, and investors. We are the world's possibility platform for the discovery, investment, fulfillment, and collection of any debt solution. At Yubi, opportunities are plenty and we equip you with tools to seize it. In March 2022, we became India's fastest fintech and most impactful startup to join the unicorn club with a Series B fundraising round of $137 million. In 2020, we began our journey with a vision of transforming and deepening the global institutional debt market through technology. Our two-sided debt marketplace helps institutional and HNI investors find the widest network of corporate borrowers and debt products on one side and helps corporates to discover investors and access debt capital efficiently on the other side. Switching between platforms is easy, which means investors can lend, invest and trade bonds - all in one place. All of our platforms shake up the traditional debt ecosystem and offer new ways of digital finance. Yubi Credit Marketplace - With the largest selection of lenders on one platform, our credit marketplace helps enterprises partner with lenders of their choice for any and all capital requirements. Yubi Invest - Fixed income securities platform for wealth managers & financial advisors to channel client investments in fixed income Financial Services Platform - Designed for financial institutions to manage co-lending partnerships & asset-based securitization Spocto - Debt recovery & risk mitigation platform Corpository - Dedicated SaaS solutions platform powered by Decision-grade data, Analytics, Pattern Identifications, Early Warning Signals, and Predictions to Lenders, Investors, and Business Enterprises So far, we have onboarded over 17000+ enterprises, 6200+ investors & lenders and have facilitated debt volumes of over INR 1,40,000 crore. Backed by marquee investors like Insight Partners, B Capital Group, Dragoneer, Sequoia Capital, LightSpeed, and Lightrock, we are the only-of-its-kind debt platform globally, revolutionizing the segment. At Yubi, People are at the core of the business and our most valuable assets. Yubi is constantly growing, with 1000+ like-minded individuals today, who are changing the way people perceive debt. We are a fun bunch who are highly motivated and driven to create a purposeful impact. Come, join the club to be a part of our epic growth story. This particular role is within our Yubi Invest vertical, and you would get to work on building our bonds platform, called Aspero, for retail users. Be able to operate in ambiguous situations and define clear objectives by breaking down the narratives independently. Work closely with business, research, data and engineering teams to understand the user goals, market dynamics and ship products. Aligning product strategy, proposition and roadmap with measurable metrics with all stakeholders. Drive PRDs, product planning, and product design of new features and enhancements. Clearly communicate product and platform benefits to our users and internal stakeholders. We're looking for a highly skilled, results-driven AI engineer who thrives in fast-paced, high-impact environments. If you are passionate about pushing the boundaries of Computer Vision, OCR, and Large Language Models (LLMs) and have a strong foundation in building and deploying AI solutions, this role is for you. As a Senior Data Scientist, you will take ownership of designing and implementing state-of-the-art OCR and Computer Vision systems. This role demands deep technical expertise, the ability to work autonomously, and a mindset that embraces complex challenges head-on. Here, you won't just fine-tune pre-trained modelsyou'll be architecting, optimizing, and scaling AI solutions that power real-world applications. Key Responsibilities: - Architect, develop, and deploy high-performance Computer Vision and OCR models for real-world applications. - Implement and optimize state-of-the-art OCR models such as Donut, TrOCR, LayoutLM, and DocFormer for document processing and information extraction. - Fine-tune and integrate LLMs (GPT, LLaMA, Mistral, etc.) to enhance text understanding and automation. - Develop custom deep learning models for large-scale image and document processing. - Build and optimize end-to-end AI pipelines, ensuring efficient data processing and model deployment. - Work closely with engineers to operationalize AI models in production (Docker, FastAPI, TensorRT, ONNX). - Enhance GPU performance and model inference efficiency, applying techniques such as quantization and pruning. - Stay ahead of industry advancements, continuously experimenting with new AI architectures and training techniques. - Work in a highly dynamic, startup-like environment, balancing rapid experimentation with production-grade robustness. Requirements: - 5-10 years experience - Proven technical expertise - Strong programming skills in Python, PyTorch, TensorFlow with deep experience in Computer Vision and OCR. - Hands-on experience in developing, training, and deploying OCR and document AI models. - Deep understanding of Transformer-based architectures for vision and text processing. - Experience working with Hugging Face, OpenCV, TensorRT, and NVIDIA GPUs for model acceleration. - Autonomous problem solver - Strong experience in scaling AI solutions, including model optimization and deployment on cloud platforms (AWS/GCP/Azure). - Thrives in fast-paced environments - Familiarity with MLOps tools (Docker, FastAPI, Kubernetes) for seamless model deployment. - Experience in multi-modal models (Vision + Text). Nice to Have: - Strong background in vector databases, RAG pipelines, and fine-tuning LLMs for document intelligence. - Contributions to open-source AI projects.,