Machine Learning Engineer
Core
Building and integrating end-to-end lifecycles of large-scale, distributed machine learning systems to extract value from data for a grocery retail provider.
Role type
Senior Machine Learning Engineer (MLOps & Distributed Systems)
Builds
Production-grade machine learning systems, automated testing frameworks, and scalable data processing pipelines.
Domain
Grocery retail / Data Engineering / Machine Learning
Deliverable
production ML models
Required skills
End-to-end ML pipeline development, MLOps, Large Language Models (LLMs) integration, Databricks Asset Bundles (DABs), Apache Spark, Python, SQL, CI/CD, containerization, cloud distributed deep learning
Preferred skills
Databricks Jobs, Azure Data Factory, Airflow, ML Flow, Delta Lake
Technologies
Databricks, Spark, Python, SQL, Azure, AWS, GCloud, ML Flow, Delta Lake, Airflow
Responsibilities
Building and integrating end-to-end lifecycles of large-scale, distributed machine learning systems; Maintaining cloud workspaces and optimizing cloud compute resources; Building automated tests and validations for machine learning models and underlying data; Maintaining production machine learning model registry and building retraining strategies; Developing and implementing methods for detecting model and data drifts; Developing scalable tools and services for handling machine learning workflows; Implementing cloud distributed approaches for deep learning models; Identifying and testing the latest technological tools that can help improve the performance and maintenance of our machine learning systems; Dealing with any unexpected pipeline production issues that might require an MLE intervention.
Seniority
Mid-to-Senior, hands-on IC
