DataNimbus - Senior Machine Learning Engineer - LLM/RAG

About DataNimbus At DataNimbus, we are on a mission to redefine how organizations leverage Data and AI to drive growth, innovation, and efficiency. Our pioneering products, such as DataNimbus Designer (a cloud-native ETL designer), datanimbus.io (a comprehensive data and integration platform), and FinHub.ai (payment modernization platform), empower businesses to simplify complex workflows, adopt cutting-edge technology, and achieve sustainable scalability. With headquarters and offices in India and Canada, DataNimbus operates globally, fostering a culture of responsible innovation, adaptability, and customer-centricity. We pride ourselves on being a trusted partner for customers navigating the complexities of Data+AI and payment modernization. Why Join DataNimbus At DataNimbus, we believe in shaping a sustainable, AI-driven future while offering an environment that prioritizes learning, innovation, and growth. Our core valuesCustomer-Centricity, Simplicity, Curiosity, Responsibility, and Adaptabilityare the foundation of our workplace, ensuring every team member can make a meaningful impact. Joining DataNimbus means being part of a dynamic team where you can : Work with cutting-edge technologies and revolutionize workflows in Data+AI solutions. Contribute to solutions that are trusted by global businesses for their scalability, security, and efficiency. Grow personally and professionally in a culture that values curiosity and continuous learning. If you're passionate about innovation, ready to solve complex challenges with simplicity, and eager to make a difference, DataNimbus is the place for you. What Do We Want You To Do Build and increase customer data science workloads and apply the best MLOps to productionize these workloads across a variety of domains. Develop LLM solutions on customer data such as RAG architectures on enterprise knowledge repos, querying structured data with natural language, and content generation. Advise data teams on several data science topics such as architecture, tooling, and best practices. Provide technical mentorship to the larger ML Subject Matter Expert community. What Would Help Make Your Case 6+ years of hands-on industry data science experience, using typical machine learning and data science tools including pandas, mlflow, scikit-learn, gensim, nltk, and TensorFlow/PyTorch. Experience building production-grade machine learning deployments on AWS, Azure, or GCP including drift monitoring. Experience with the latest techniques in natural language processing including vector databases, fine-tuning LLMs, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI. Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research) or equivalent practical experience. Experience communicating and teaching technical concepts to non-technical and technical audiences alike. Passion for collaboration, life-long learning, and driving value through ML. Can meet expectations for technical training and role-specific outcomes within 3 months of hire. (ref:hirist.tech)