AI Applications Engineer

About WareIQ

WareIQ is a technology-first e-commerce fulfillment company, backed by Y Combinator and Flexport. We run a distributed fulfillment network across 13+ cities in India, processing orders for 400+ consumer brands across Amazon, Flipkart, Meesho, Shopify, and quick commerce channels. Our core infrastructure spans warehouse management, inventory routing, order orchestration, and carrier integrations across 27,000+ pincodes.

We are actively building AI-powered systems on top of this operational data layer.This role sits at the center of that build-out.

The Role

We are hiring an AI Applications Engineer to build internal AI-powered systems at WareIQ. This is not a research role and not a pure backend role. It sits at the intersection of statistical modeling, AI integration, and practical software engineering. You will build systems that run on real operational data and produce decisions that affect how we run our fulfillment network.

The role has two equal and non-negotiable tracks.

Track 1: Statistics and AI

You understand statistical modeling at a level where you can reason about model outputs, not just run them. You know what a model is doing mathematically, why it might fail on a particular data shape, and how to design a validation framework that gives you honest signal.

You will be expected to:

• Apply time series forecasting and regression models to operational data, and critically evaluate which model performs better on accuracy, bias, and stability
• Understand core statistical concepts: hypothesis testing, distributional assumptions, confidence intervals, overfitting, and error decomposition
• Classify data patterns (stable, seasonal, intermittent, spiky) and match modeling approaches accordingly
• Integrate LLMs via API into operational workflows. This means structured prompting, output parsing, and building AI-assisted features into backend systems, not prompt engineering as an end in itself

Track 2: Backend Development and Data Engineering

You can build the full stack from data ingestion to usable output. You understand how to connect to external commerce APIs, normalize messy transactional data, run models against it at scale, and surface results in a functional interface.

You will be expected to:

• Build Python data pipelines that are clean, reliable, and capable of handling large SKU catalogs across multiple data sources
• Integrate with e-commerce platform APIs (Shopify, Amazon SP-API, or similar) to pull order history, inventory levels, and catalog data and structure it into analysis-ready datasets
• Design and maintain data schemas that support recurring model runs and output tracking
• Build lightweight internal interfaces using React, Streamlit, or equivalent tools. These do not need to be design-polished. They need to work and be usable by non-engineers
• Work with scheduled jobs and cloud infrastructure at a practical level

What We Are Looking For

The strongest candidates will have built something end-to-end where they were responsible for both the statistical logic and the code that executed it. That could be a forecasting system, an internal analytics tool, an operations automation, or something similar. You should be able to explain why a model made a specific prediction on a specific dataset and also explain the pipeline architecture that produced that result.

What This Is Not

This is not a data science role where you hand implementation to engineers. This is not a backend role where someone else owns the modeling layer. You own both.

Details

• Location: Remote, 
• Type: Full-time