Nextuple is on a mission to level the playing field for omnichannel retailers. Nextuple helps retailers create and transform omnichannel order management by using a microservices architecture. The Nextuple OMS Studio enables retailers to quickly build and scale new order management and inventory-led experiences to delight customers, create more omnichannel agility, and accelerate time-to-value.

Nextuple is about more than just creating value for our clients. We're also dedicated to developing ourselves as experts and creating ideas that can change omnichannel order management for the better.

Our experience in omnichannel fulfillment is a big reason clients hire us. But we never rest on our laurels; expertise must be continuously developed. As retail changes and grows, Nextuple is constantly challenging the norm, trying new things, testing, and learning. Exponential growth is so important that it's built into our name (the ex in our logo). We create opportunities that compound value for our clients.

Our agility and ingenuity, combined with our products, hard work, and strategic partnerships, allow us to deliver greater value than anyone else. Whether it's stepping into a customer's shoes to understand their problems, supporting a team member who needs help, or standing behind a partner who's having a tough day, we always exercise empathy. Nextuple isn't a data, technology, or logistics company. We're a people company; we have been and always will be.

Job Description :

The ML Engineer (Forecaster) will build and deploy intelligent forecasting and agentic AI systems that drive real supply chain decisions for enterprise clients.

Responsibilities :

- Designing agentic AI pipelines that reason, decide, and adapt autonomously.

- Building advanced demand forecasting models using attribute-based and cohort-based approaches.

- Integrating external signals and real-time market inputs into forecasting frameworks.

- Feature engineering at scale : lag structures, rolling aggregations, and derived features.

- Connecting model outputs to measurable business outcomes and communicating insights to clients and leadership.

Requirements :

- Strong foundation in Python (pandas, scikit-learn, XGBoost, LightGBM).

- Understanding of time series forecasting and experience with large-scale transactional datasets.

- Genuine curiosity about agentic AI, LLMs, and emerging ML paradigms.

Bonus :

- Exposure to retail/supply chain domains, hierarchical forecasting, or LLM-based agent frameworks.