MLOps Engineer

Top Skills - Must Haves

Ecommerce in the Recent Project

• Google Cloud Platform
• ci/cd pipelines
• mlops
• data pipeline
• sql
• machine learning
• Data Science
• vertex ai
• databricks
• Spark
• mlflow

Top Skills' Details

ML Data Engineering, Pyspark, Python, Google Cloud Platform

Machine Learning Engineer – Recommender Systems & Feature Platform
Overview
Client is looking for a Machine Learning Engineer to support and mature our recommender systems with a strong focus on feature engineering, data management, and feature store operations. This role will be critical to scaling high quality machine learning features, improving reuse across teams, and establishing strong governance and operational support practices across ML platforms.
You will work closely with data scientists, data engineers, MLOps, and platform teams to deliver reliable, governed, and cost aware feature pipelines that power personalization and recommendation use cases.
Core Skills
Strong experience with feature engineering and data pipelines for machine learning systems.
Proficiency in Python, including writing production‑quality, testable code.
Experience working with large datasets, feature transformations, and data validation.
Familiarity with recommender systems or personalization use cases (ranking, retrieval, or feature‑driven models).
Cloud & Platform
Hands‑on experience with Google Cloud Platform (Google Cloud Platform).
Practical experience with Vertex AI, including training workflows, pipelines, and feature store concepts.
Experience using Databricks (Spark, notebooks, ETL workloads) for feature development and data processing.
Data & Operations
Experience managing feature and data pipelines in production environments.
Understanding of governance, lineage, and access control for ML data and features.
Experience supporting production systems, including monitoring, alerting, and issue triage.
Familiarity with cost/expense scaling models and optimizing data and ML workloads for efficiency.
Nice to Have
Experience with ML Feature Stores (Vertex AI Feature Store or similar).
Exposure to MLOps practices, CI/CD, and automated testing for ML systems.
Knowledge of streaming pipelines and real‑time feature delivery.
Experience working in large, cross‑functional enterprise ML platforms.
Key Responsibilities
Design, build, and maintain machine learning features for large‑scale recommender systems (batch and real‑time).
Develop and operate feature pipelines that support training and online inference consistency.
Contribute to the maturation of Kroger’s ML feature store, including feature reuse, documentation, versioning, and lifecycle management.
Partner with data science and platform teams to translate modeling needs into production‑ready features.
Implement and enforce data and feature governance, including lineage, access controls, and compliance requirements.
Support issue triage and operational support for feature pipelines, training jobs, and inference dependencies.
Monitor and optimize data quality, performance, and cost, including expense‑aware scaling of feature and model workloads.
Collaborate on platform standards, best practices, and operational runbooks across ML teams

Additional Skills & Qualifications

Must be strong fo Google Cloud Platform (Manager does not want to see AWS)

Employee Value Proposition (EVP)

Opportuity to work with leader in grocery retail on their Recommender Systems & Feature Platform