Senior MLOps Engineer: Logistics AI Infra

Veho; Northern (KY); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Equity

Medical/Dental/Vision

401(k)

Unlimited PTO

Job summary

Veho is seeking a Senior Machine Learning Operations Engineer to build and scale ML infrastructure within its logistics-focused tech stack. You will work with data scientists and software engineers to deploy, monitor, and improve production models that optimize routing, pricing, and network performance.

The role emphasizes end-to-end ML lifecycle governance, robust data pipelines, and collaboration across Data Science and Engineering teams.

Qualifications

• Bachelor’s degree plus 3+ years in ML engineering or Master’s plus 2+ years.
• Developing and optimizing MLOps pipelines for speed, reliability, observability.
• Apply statistical modeling or ML to solve business problems.
• Strong Python and SQL proficiency.
• Experience with open-source ML tooling for large-scale systems.
• Experience with data warehouses and cloud-based data tools.
• AWS or similar cloud experience preferred.
• Startup experience is a plus, logistics/supply chain exposure is a plus.

Responsibilities

• Build reliable, scalable ML infrastructure for AI/ML capabilities.
• Create robust data pipelines to feed analyses and models.
• Enable forecasting, network orchestration, and live pricing systems.
• Ensure data quality and data integrity with best practices.
• Build feature stores, model orchestration and monitoring tooling.

Skills

Python

SQL

ML Engineering

MLOps

Data Pipelines

AWS Cloud

Model Monitoring

Team Collaboration

Education

Bachelor’s Degree in CS/Math/Engineering

Master’s Degree in CS/ ML

Tools

Ray

Flink

Feast

Redshift

Databricks

Snowflake

Job description

Veho is seeking a Senior Machine Learning Operations Engineer to build and scale ML infrastructure within its logistics-focused tech stack. You will work with data scientists and software engineers to deploy, monitor, and improve production models that optimize routing, pricing, and network performance.

The role emphasizes end-to-end ML lifecycle governance, robust data pipelines, and collaboration across Data Science and Engineering teams.