We are seeking an experienced Full-Stack Software Engineer to build the software ecosystem powering our next-generation AI Vision Systems. You will develop the "connective tissue" between high-performance machine learning models running on edge hardware and our Google Cloud-based analytics backend. This is a hands-on role for an engineer who is passionate about bringing AI out of the lab and into the real world.

Required Qualifications:

• Experience:

3+ years of professional software engineering experience in a production environment.

• Edge Development:

Proven experience deploying software to edge computing hardware or IoT devices.

• Backend Mastery:

Strong proficiency in Python (required) and at least one other language (C++, Go, or Node.js).

• Cloud Fluency:

Experience building on Google Cloud Platform (GCP) or similar (AWS/Azure), specifically with managed database services.

• Modern Frontend:

Experience building responsive web applications with React or similar modern frameworks.

• DevOps Basics:

Familiarity using docker as the key configuration, build, and deploy mechanism, CI/CD pipelines and disciplined version control approach (GIT based)

Desired Skills:

• Experience with OpenCV, TensorRT, or OpenVINO for vision optimization.
• Familiarity with ML frameworks like Py Torch or Tensor Flow.
• Knowledge of industrial protocols (MQTT, WebSockets) for real-time data streaming.
• A passion for "Agentic" workflows and continuous improvement.

Responsibilities:

• Edge Software Integration:

Develop and optimize software to deploy machine learning models on edge devices (NVIDIA Jetson/Thor), ensuring low-latency performance for real-time vision tasks.

• Full-Stack API Development:

Build scalable RESTful APIs and microservices (Python/C++) that allow edge devices to communicate seamlessly with cloud backends.

• Data Architecture:

Design and manage data pipelines using Google Cloud tools (Big Query, Postgres) to handle real-time image/video data and model telemetry.

• Web Interfaces:

Create intuitive, high-performance web-based dashboards (React/TypeScript) for monitoring system health and visualizing AI-driven insights.

• AI-Augmented Engineering:

Heavily leverage Agentic AI tools and LLM-assisted workflows to accelerate development cycles and maintain high code quality.

• Incremental and Iterative Delivery:

Work with the team and key stakeholders to find and deliver product increments in an iterative way, taking reasonable risks, validating key hypothesis, and learning continuously

• Cross-Functional Deployment:

Collaborate with Data Scientists to containerize models (Docker/Kubernetes) and with Hardware Engineers to validate performance on the factory floor.