Backend Engineer

About SimplismartA bit about our product - Simplismart is an MLOps platform with 3 major suites:Training suite: Assemble and train any model, including LLMs, vision, audio, tabular, and tree models.Deployment suite: Most companies fail to make models production-ready. Our proprietary model deployment suite is 6x faster than HuggingFace's enterprise suite and 12x faster than replicate.ai. Users can easily deploy (auto-scale) models trained on Simplismart (more optimised), import any model from HuggingFace, or even a Pytorch/Tensorflow artefact: Tensorflow, Pytorch, ONNX, JAX.Observability suite: Monitor model health, including load, latency, uptime, data drift, and concept drift. This is an excellent opportunity for individuals looking to be part of a 0→1→10 in the next 2 years who want to experience what it is to build a product/business from scratch and hopefully start their own venture someday. Not so much for individuals who prefer stability over growth! Job Description This role requires a strong background in machine learning, proficiency in relevant programming languages and tools, a willingness to embrace challenges, and a commitment to the best software development and testing practices. Additionally, familiarity with cloud platforms and a dedication to staying current with industry trends are essential for success in this role. Who we are looking for: Python Experience: 2-5 years of experience with Python. Generative AI Experience: You must be proficient in LLMs like Llama and Mistral and other Generative AI models like Whisper and Stable Diffusion. Cloud Experience: You should be familiar with cloud computing platforms, with a preference for expertise in AWS and knowledge of platforms like Google Cloud Platform (GCP) or Microsoft Azure. Experience with Docker and Kubernetes: You should be proficient in Docker and Kubernetes. Test-Driven Development: Belief in and adherence to Test-Driven Development practices is essential. This means writing tests before writing code to ensure the quality and correctness of your work. Good to Have: Deep understanding of GPU Architecture: You should have a deep knowledge of GPU Architectures like A100, A10G, and T4 chips. Experience with CUDA is a plus. Familiarity with LLM optimization techniques: Good enough idea of optimisation techniques like Quantization, speculative decoding, continuous batching, etc. Responsibilities: Design and Develop Scalable Machine Learning Systems: You will be responsible for collaborating with the tech team to design and build machine learning systems that are scalable and ready for production use from the start. This involves the end-to-end development of machine learning models and pipelines. You should be able to deploy and benchmark an ML model in under 30 minutes. Conduct Extensive Research: You'll need to stay current with the latest machine learning technologies and research to identify the best approaches and tools for the job. Improve Metrics: You will develop strategies for improving metrics using real-world data. This likely involves optimizing and fine-tuning machine learning models to achieve better results. Infrastructure Improvements: You'll assist in enhancing and extending existing infrastructure, which may involve adding new features and optimizing performance. Why should you join SimpliSmart? Well, let's break away from the conventional perks and instead focus on what you WON'T experience here: Legacy System Headaches: You won't have to endlessly grapple with outdated legacy systems that hinder your productivity and creativity. Bossy Culture: At SimpliSmart, we believe in collaboration and empowerment, not hierarchy. You won't have a boss breathing down your neck but instead, colleagues who support your growth. Dark Circles: Late nights and overwork are not the norm here. We prioritize work-life balance, ensuring you won't be sporting those tired, dark circles under your eyes. Stagnation: Say goodbye to redundant and stagnant tasks. We thrive on innovation and dynamic challenges that keep you engaged and motivated.