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), 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 such as architecture, tooling, and best practices

- Provide technical mentorship to the larger ML Subject Matter Expert community

What would help make your case :

- 3- 5 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

- [Preferred] Experience working with Apache Spark to process large-scale distributed datasets

- [Preferred] Experience working with the Databricks platform

- [Preferred] 2+ years customer-facing experience in a pre-sales or post-sales role

- Can meet expectations for technical training and role-specific outcomes within 3 months of hire