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)
