MLOps Engineer: Data Pipelines, CI/CD & Model Deployment

XPO; Boston (MA); Hybrid; USD 100,000 - 120,000; Full time

Benefits offered by this job

Full health insurance

PTO 15 days

9 paid company holidays

401(k) with company match

Education assistance

Job summary

XPO in Boston, MA is seeking a Machine Learning Engineer to build and maintain ML data prep and validation tooling and ML infrastructure for training, evaluation, deployment, and MLOps, including CI/CD, monitoring and feedback loops.

You'll collaborate with data scientists and data engineers to productionize models and ensure reliable data pipelines, contributing to shared MLOps tooling across the AI/ML organization.

Qualifications

• Hands-on experience building data pipelines and ML infrastructure.
• Proficiency in Python and SQL for data and model workflows.
• Experience with cloud data/ML platforms (AWS/GCP/BigQuery).
• Collaborative, cross-functional work across data science and data engineering.
• 3+ years building ML infrastructure for training, evaluation, and deployment.

Responsibilities

• Build and maintain data prep and validation tooling for ML inputs.
• Design ML infrastructure for training, evaluation, and deployment.
• Develop CI/CD pipelines and automated testing for ML models.
• Implement model monitoring, drift detection, and feedback loops.
• Collaborate to productionize models and streamline experimentation-to-production.

Skills

Data prep tooling

ML infrastructure

CI/CD pipelines

Model monitoring

Drift detection

Feedback loops

Productionize models

Data pipelines

MLOps tooling

Education

Bachelor's degree in CS or related

Master's degree in CS or related

Tools

Python

SQL

AWS

GCP

BigQuery

Docker

Kubernetes

Job description

XPO in Boston, MA is seeking a Machine Learning Engineer to build and maintain ML data prep and validation tooling and ML infrastructure for training, evaluation, deployment, and MLOps, including CI/CD, monitoring and feedback loops.

You'll collaborate with data scientists and data engineers to productionize models and ensure reliable data pipelines, contributing to shared MLOps tooling across the AI/ML organization.