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 in the U.S. 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 values—Customer-Centricity, Simplicity, Curiosity, Responsibility, and Adaptability—are 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.

Key Responsibilities:

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.

Required Qualifications:

5-8 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

Interested

Send in your CV to [HIDDEN TEXT] ASAP!

Senior Machine Learning Engineer