Machine Learning Engineer - Hybrid Remote

We are a B2B startup providing leading supermarkets with a smart shopping assistant designed to maximize customer loyalty. Our technology doesn't just fill a cart; We are looking for a

Machine Learning Engineer

with strong expertise in data engineering and modeling to take our prediction and recommendation systems to the next level. You will join an environment where data flow is critical. Your main goal will be to optimize, maintain, and evolve our Machine Learning pipelines on

Databricks

, ensuring that demand forecasts and product recommendations are accurate, scalable, and delivered to our application on time.

&##Language:

Python (Expert), PySpark. Databricks (Workflows, Unity Catalog, model serving). LightGBM (distributed on Spark), Sklearn. Database:

MongoDB (serving layer).

&##ML Engineering on Spark:

Implement and optimize

LightGBM

models in a distributed environment using Spark, ensuring efficiency in both training and inference times. Maintain and build ETL workflows for data ingestion and feature engineering. Data Integration:

Manage the efficient export of scoring results to

MongoDB

for real-time consumption by the App. MLOps & Quality:

Use

MLflow

to manage the model lifecycle, monitor data drift, and ensure experiment reproducibility.

&##Experience:

Minimum 2 years in a Machine Learning Engineer or Data Scientist role with a strong engineering component. Python & Spark:

Advanced command of Python and proven experience processing large datasets with

Apache Spark

(PySpark). Software Engineering:

Professional proficiency in English (written and spoken).

If you are passionate about data and our mission, but don't meet every single requirement listed above, please apply. Knowledge of MongoDB query structure and optimization. Data Visualization:

Databricks SQL, Streamlit, or Tableau) to visualize model metrics and business KPIs. Work with a modern tech stack (Databricks, Spark, MLflow) and real, high-volume data. Remote/Hybrid Culture:

We support a hybrid or remote working model, allowing you to work from where you are most productive.