Edge AI Vision Full-Stack Engineer — Real-Time Cloud & Edge

Ford México; Naucalpan de Juárez; On-site; MXN 900,000 - 1,300,000; Full time

Job summary

Ford México is seeking an experienced Full-Stack Software Engineer to build the software ecosystem powering AI vision systems. You will connect edge ML models with a Google Cloud analytics backend, and develop responsive dashboards to monitor system health and insights.

Responsibilities include deploying to edge devices, building RESTful APIs, and managing data pipelines. Collaboration with data scientists and hardware teams is essential for real-world factory environments.

Qualifications

• 5+ years of professional software engineering experience.
• Experience deploying to edge hardware or IoT devices.
• Proficiency in Python and at least one other language (C++, Go, or Node.js).
• Cloud experience with GCP/AWS/Azure and managed databases.
• Frontend experience with React or similar frameworks.
• Familiarity with Docker, CI/CD, and Git.
• Experience with OpenCV, TensorRT, or OpenVINO.
• Knowledge of ML frameworks like PyTorch or TensorFlow.
• Knowledge of MQTT/WebSockets for real-time data streaming.
• Passion for Agentic workflows and continuous improvement.

Responsibilities

• Edge software integration and optimization for real-time vision tasks.
• Full-stack API development in Python/C++ with cloud backends.
• Design data pipelines using Google Cloud tools for real-time data.
• Build web dashboards with React/TypeScript for monitoring.
• Containerize models with Docker/Kubernetes and collaborate with hardware engineers.

Tools

Python

C++

Go

Node.js

React

TypeScript

Docker

Kubernetes

Git

OpenCV

TensorRT

OpenVINO

PyTorch

TensorFlow

MQTT

WebSockets

Google Cloud Platform

AWS

Azure

CI/CD

NVIDIA Jetson

Docker/Kubernetes

Job description

Ford México is seeking an experienced Full-Stack Software Engineer to build the software ecosystem powering AI vision systems. You will connect edge ML models with a Google Cloud analytics backend, and develop responsive dashboards to monitor system health and insights.

Responsibilities include deploying to edge devices, building RESTful APIs, and managing data pipelines. Collaboration with data scientists and hardware teams is essential for real-world factory environments.