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# VP, AI & Platforms

- **Company:** Veho Tech
- **Location:** New York, NY, United States
- **Salary:** $275,000.0 - $325,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Azure Machine Learning
- **Published:** June 29, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=2a5c8461c0cc5364

## About the Role

Do you have experience in Team development?, * Experience at companies with strong ML Platform / MLE cultures

 * Has led engineering or Data Science and understands what it means to scale
 * Experience building or leading distributed and remote teams
 * Background in logistics, delivery, or marketplace tech

## Description

Time-to-insight cut in half for data teams. Customer critical data science projects shipped on-time and with lower lift. Experimentation becomes a first-class citizen. Accelerate impact by moving key company metrics in the right direction.

Agents handle >75% of coding tasks. A no-code platform powered by AI agents enables builders across the company, not just engineers, to ship value. Data is at everyone's fingertips through an AI-powered data explorer. A high-functioning global team ships software and data science models at a high velocity. Product Engineering and Data Science can't imagine working without the platforms your teams ship. You'll know you're winning when the way Veho builds software looks nothing like it does today.

What a Great Candidate Looks Like

You are a builder. You get your hands in code, you prototype, you ship. You show up with PRs, not slides. You've built platforms that other teams depend on and you know what it takes to earn adoption through excellence. Your teams respect you for your technical grasp.

You've built and scaled teams. You know how to hire, develop people, and make hard calls. You've led distributed teams and know what breaks when you scale. You've seen different stages - what works at 10 doesn't work at 100, and you've learned that firsthand. Your success is measured by what your teams ship.

You have deep technical roots. You can have a technical conversation with a data scientist or ML engineer without pulling someone else in. You've likely come from an MLE or ML-adjacent background and understand the friction between engineering and data science because you've lived on both sides.

You move fast. You ship value in weeks, not quarters. You're comfortable with explicit tech debt cycles; working solution first, best solution later.


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