Senior MLOps Engineer: Build Scalable AI for Logistics

Veho; United States; On-site; USD 150,000 - 210,000; Full time

Job summary

Veho is seeking a Senior Machine Learning Operations Engineer to embed with data scientists and software engineers, building the infrastructure to deploy, monitor, and scale ML models in production. You will own production models and collaborate closely to ensure peak performance and rapid incident response.

You will contribute across ML platform work and model-building layers, enabling forecasting, orchestration, and data integrity while fostering a productive, impact-driven culture.

Qualifications

• Bachelor's degree or Master's with relevant ML engineering experience.
• Experience developing and optimizing MLOps pipelines for speed, reliability, and observability.
• Proficiency with Python and SQL.
• Hands-on experience with large-scale ML tooling (e.g., Ray, Flink, Feast).
• Experience with data warehouses (e.g., Redshift, Databricks, Snowflake).
• Cloud-based data engineering/science tools; startup ML systems a plus.

Responsibilities

• Build reliable, scalable ML infrastructure for AI/ML capabilities.
• Create data pipelines to feed analyses and models.
• Enable forecasting, network orchestration, and live pricing systems.
• Ensure data quality and integrity through best practices in data integration.
• Develop feature stores, model orchestration tooling, and monitoring.
• Create templates for model development and deployment across teams.

Skills

ML engineering

MLOps pipelines

Python

SQL

Data pipelines

Data quality

Model monitoring

Cloud data engineering

Startup experience

Logistics domain

Education

Bachelor's Degree in a relevant field

Master's Degree in a related field

Tools

Ray

Flink

Feast

Redshift

Databricks

Snowflake

AWS

Job description

Veho is seeking a Senior Machine Learning Operations Engineer to embed with data scientists and software engineers, building the infrastructure to deploy, monitor, and scale ML models in production. You will own production models and collaborate closely to ensure peak performance and rapid incident response.

You will contribute across ML platform work and model-building layers, enabling forecasting, orchestration, and data integrity while fostering a productive, impact-driven culture.