ML Engineer / ML Platform Engineer

Tap Growth ai; Austin (TX); On-site; USD 140,000 - 210,000; Full time

Job summary

H-E-B is seeking a hands-on Senior Machine Learning Engineer to build and scale personalization, recommendation, and ranking capabilities for digital commerce. You will develop ML pipelines, deploy production models, and support large-scale customer experiences across web and mobile channels.

You will work across data engineering, ML, and platform teams to deliver real-time personalization, experimentation, and cloud modernization while ensuring robust governance and CI/CD maturity.

Responsibilities

• Develop and deploy: Recommendation models
• Ranking models
• Customer segmentation models
• Propensity models
• Personalization services
• Build real-time recommendation systems leveraging streaming customer events
• Create large-scale batch processing pipelines for customer behavior analysis and model training
• Engineer and manage feature pipelines for production ML systems
• Implement MLOps best practices including CI/CD, monitoring, retraining, testing, and governance
• Partner with product managers, data scientists, and engineers to launch new personalization capabilities
• Conduct A/B testing and experimentation to optimize customer outcomes
• Deploy models into production environments and monitor model performance
• Support cloud modernization initiatives and migration of ML workloads

Skills

Python

Spark

Databricks

Kafka

TensorFlow

PyTorch

MLOps

Job description

Overview

Hands-on ML engineer responsible for building recommendation pipelines, deploying machine learning models, engineering data platforms, and supporting personalization initiatives at scale.

H-E-B is seeking a Senior Machine Learning Engineer to build and scale personalization, recommendation, and ranking capabilities supporting digital commerce initiatives. This engineer will develop machine learning pipelines, deploy production models, and help deliver next-generation customer experiences across web and mobile channels.

The ideal candidate is a hands-on engineer with expertise across machine learning, data engineering, MLOps, and large-scale distributed systems.

Key Responsibilities

• Develop and deploy:
  • Recommendation models
  • Ranking models
  • Customer segmentation models
  • Propensity models
  • Personalization services
• Build real-time recommendation systems leveraging streaming customer events.
• Create large-scale batch processing pipelines for customer behavior analysis and model training.
• Engineer and manage feature pipelines for production ML systems.
• Implement MLOps best practices including CI/CD, monitoring, retraining, testing, and governance.
• Partner with product managers, data scientists, and engineers to launch new personalization capabilities.
• Conduct A/B testing and experimentation to optimize customer outcomes.
• Deploy models into production environments and monitor model performance.
• Support cloud modernization initiatives and migration of ML workloads.

Required Technology Stack

• Python
• Spark
• Databricks
• Kafka
• TensorFlow and/or PyTorch
• MLOps

Preferred Cloud Experience

• AWS
• BigQuery
• Pub/Sub
• Dataflowoops