Technical Lead Manager, Machine Learning Operations

About Veho

About The Role:

• Is an expert in their craft, creating high quality ML infrastructure and delivering impactful machine learning models to our stakeholders. 
• Works in close collaboration with the other Data Science team members and keeps the business value at the center of their work. Has a bias for action, balancing delivering impact in the short-term while building out the long term vision.
• Applies their ML / MLOPS knowledge to suggest new patterns, tools, approaches to improve the team’s models
• Drives team velocity by helping the current team develop in their careers, hiring strong new talent onto the team, and adopting AI as a core part of development.

• Lead and grow a team of four engineers spanning ML infrastructure, ML operations, and embedded data science project work.
• Improve our internal ML platform: standardize and improve ML infrastructure, improve how DS services are created, deployed, and operated. Think service performance, permissioning, environment setup, and integration with upstream and downstream systems.
• Set the roadmap for improving our Machine Learning and Operations Research infrastructure.
• Embed engineers into major science initiatives (forecasting, network orchestration, pricing) so every project is technically sound and lessons learned find their way back into our platform.
• Drive AI usage across DS. Collaborate with our Agentic Developer Experience team to ensure new tooling has a high impact on the Data Science team’s velocity. Set standards, introduce patterns, and drive adoption of how to leverage AI in data science workflows (EDA, model iteration, ML/OR methodologies)
• Be part of the on-call rotation for our data science production systems.

• Bachelor’s Degree plus at least 6 years of experience in Machine Learning Engineering, or Master’s Degree plus at least 4 years in Machine Learning Engineering:
• This experience should include:
  • ML platform experience: training and serving infrastructure, feature stores, orchestration, monitoring, deployment pipelines
  • experience managing impactful, high velocity ML Platform / ML Ops teams in smaller scale companies
  • experience driving AI/agentic tooling adoption inside an organization
• hands‑on experience with open‑source tooling for large‑scale ML (e.g., Ray, Flink, Feast).
• strong knowledge of Cloud‑based data engineering and data science tools (AWS preferred) and Data Warehouses (Redshift, Databricks, Snowflake).
• Strong proficiency in Python.
• Interest in building systems in a Supply Chain setting, enabling a physical supply chain to run like clockwork.