3 Edge Deployments Jobs

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.,

As a part of ZebiOps Technology, the creators of ZORA, a privacy-first AI voice assistant dedicated to simplifying digital lives, we are seeking a proactive and hands-on CTO & Co-Founder to take charge of the end-to-end product architecture and execution of ZORA's journey from inception to growth. Your responsibilities will include leading the architecture and full-stack product execution right from the conceptualization phase to the final product launch. You will play a crucial role in constructing robust, scalable, and secure backend systems integrated with AI technology. Additionally, you will be responsible for recruiting, guiding, and expanding an efficient engineering team. Alongside the founder, you will be instrumental in shaping the technology vision and product strategy. To excel in this role, you should possess over 10 years of experience in software engineering and system/product architecture. A proven track record of successfully developing and delivering end-to-end products is essential. Expertise in building scalable backend systems, integrating AI/ML technologies, and deploying on cloud/edge platforms is highly desirable. As a hands-on builder, you should exhibit the dedication and enthusiasm to translate ideas into revolutionary market products. By joining our team, you will have the opportunity to become a co-founder with equity in a rapidly growing AI startup. You will be involved in building a pioneering AI assistant from scratch, defining a new category in the industry. Your role will allow you to take charge of the technological vision, execution, and impact of a product envisioned to reach millions of users. If you are passionate about leading technological innovations and eager to be part of a groundbreaking AI venture, we encourage you to apply or connect with us at prashant@zebiops.com.,

You will be joining our team as a skilled Deep Learning Engineer with expertise in object detection and segmentation models. Your primary responsibilities will include implementing and refining object detection models such as YOLOvX, Faster R-CNN, EfficientDet, SSD, and Mask R-CNN. Additionally, you will work on real-time computer vision applications, optimize performance, annotate and prepare datasets, and collaborate on research and development projects to enhance model performance and robustness. As a Deep Learning Engineer, you will be expected to deploy models using Docker on Linux/Windows systems, with experience in edge deployment considered a plus. It will be essential for you to document code, experiments, and deployment processes while collaborating with cross-functional teams. Strong Python programming skills, knowledge of TensorFlow, PyTorch, OpenCV, and ONNX, as well as hands-on experience with Docker and familiarity with model optimization techniques like quantization and pruning are required for this role. An advantage would be your experience in edge deployments using platforms such as NVIDIA Jetson, TensorRT, and OpenVINO. Additionally, familiarity with experiment tracking tools like MLflow or Weights & Biases is a plus. The qualifications we are looking for include a Bachelors or Masters degree in Computer Science, AI, Data Science, or a related field, along with strong analytical, problem-solving, and team collaboration skills. This is a full-time position with a day shift schedule that requires in-person work at our location.,