As a Developer/Senior Developer at Krutrim, you will be responsible for developing embedded software for different processors. You should have good knowledge in developing drivers for various hardware blocks such as PCIe, I2C, USB, UART, Ethernet, and Crypto security. Understanding Linux kernel internals and working with open-source software is essential for this role. You will be involved in developing high and low-level designs, drivers, and firmware for different hardware blocks. Additionally, you will work on adopting operating systems and embedded software for various processor architectures. Your responsibilities will include developing software based on pre-silicon development vehicles, bringing up software using pre-silicon vehicles and silicon-based platforms, upstreaming open-source code, developing software component-level tests, and enabling them in a CI/CD system. You will also be debugging issues using standard hardware/software-based debuggers and diagnostic equipment. To excel in this role, you should possess excellent knowledge of Linux internals, different drivers, and standards like PCIe, Ethernet, and CXL. Expertise in the Software Development Life Cycle (SDLC), firmware BSP, device drivers, and strong technical problem-solving skills in areas like system boot, UEFI, and OS functionality are crucial. You should have software development skills in C, C++, and Python, along with strong low-level debugging capabilities. A successful candidate for this position should hold a BTech/MTech in Computers, Electronics, or Electrical Engineering and have around 5-12 years of experience in embedded software development across different architectures. Having a good understanding of different CPU architectures like IA, ARM, and RISC V, as well as the Pre-Silicon Development environment, will be advantageous in fulfilling the requirements of this role.,

You are an experienced Lead Generative AI Engineer responsible for training, optimizing, scaling, and deploying various generative AI models including large language models, voice/speech foundation models, vision, and multi-modal foundation models using cutting-edge techniques and frameworks. Your role involves architecting and implementing state-of-the-art neural architecture, robust training, and inference infrastructure to efficiently take complex models with billions of parameters to production while optimizing for low latency, high throughput, and cost efficiency. Your key responsibilities include: - Architecting and refining foundation model infrastructure to support optimized AI models deployment focusing on C/C++, CUDA, and kernel-level programming enhancements. - Implementing optimization techniques like quantization, distillation, sparsity, streaming, and caching for model performance enhancements. - Spearheading the development of Vision pipelines to ensure scalable training and inference workflows of billions of parameter foundation models. - Innovating for state-of-the-art architectures involving Panoptic Segmentation, Image Classification, and Image Generation. - Designing, developing, and innovating state-of-the-art large multimodal models. - Executing training and inference processes to minimize latency and maximize throughput utilizing GPU clusters and custom hardware. - Integrating and tailoring frameworks like PyTorch, TensorFlow, DeepSpeed, Lightening, FSDP, and Habana for fast model training and inference. - Enhancing post-deployment mechanisms with exhaustive testing, real-time monitoring, and robustness checks. - Driving continuous improvement initiatives for deployed models with automated pipelines for drift detection and performance degradation. - Leading the charge in model management including version control, reproducibility, and lineage tracking. - Cultivating a culture of high-performance computing and optimization within the AI/ML domain. Qualifications: - Ph.D. with 5+ years or MS with 8+ years of experience in ML Engineering, Data Science, or related fields. - Demonstrated expertise in high-performance computing with proficiency in Python, C/C++, CUDA, and kernel-level programming for AI applications. - Extensive experience in optimizing training and inference for large-scale AI models. - Understanding of Diffusion Models, Variational Autoencoders, Bayesian Modelling, and Reinforcement Learning is beneficial. - Experience in building billions of parameters generative AI foundation models. - Proven success in deploying optimized ML systems on a large scale using cloud infrastructures and GPU resources. - In-depth understanding and hands-on experience with advanced model optimization frameworks and MLOps tools. - Familiarity with contemporary MLOps frameworks and their application in production environments. - Strong grasp of state-of-the-art ML infrastructures, deployment strategies, and optimization methodologies. - Innovative problem-solving skills and collaborative mindset. - Exceptional communication and team collaboration skills.,