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ML Engineer / ML Platform Engineer

Rapsys TechnologiesDallas, TX🇺🇸United States

Quick overview

SeniorityMid Senior
Work modeRemote
LocationDallas, TX, United States
AWSMLOpsBigQueryDatabricksGoogle CloudKafkaPyTorchPythonTensorFlowMachine LearningCI/CD

Job description

Senior ML Engineer / ML Platform Engineer

Location: Remote but needs to live in Austin, San Antonio and Dallas

Open to: Austin, San Antonio or Dallas, TX offices – can be mostly remote but needs to live in one of those areas to go into meetings occasionally AND if they convert to FTE, there will be return to office 5 days a week starting Sept 2027

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
  • Model Deployment
  • Feature Engineering

Preferred Cloud Experience

  • AWS
  • BigQuery
  • Vertex AI
  • Pub/Sub
  • Dataflow
  • GKE
  • Cloud Run
  • AWS $B"*(B Google Cloud Platform migration experience

Target Background

  • 4-8 years of experience
  • ML Engineering, MLOps, or Data Platform Engineering
  • Recommendation systems or personalization experience
  • Retail/eCommerce customer analytics preferred

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