Edge AI/Computer Vision Engineer

Concept Reply, part of the Reply Network, is a leader in IoT solutions. We support and advise our customers in Automotive, Manufacturing, Smart Infrastructure and beyond in all aspects of Internet of Things (IoT) and Cloud Computing. From designing and developing customized IoT solutions to implementing and managing them seamlessly, Concept Reply helps customers unlock the potential of IoT.

We are seeking a Computer Science or Computer Engineering graduate for an engagement deploying AI-driven computer vision for industrial safety, working alongside our data and AI engineering team and the client's controls engineers on real, on-site deliverables.

Responsibilities

• Install, configure and calibrate smart cameras at manufacturing sites for human detection in restricted areas
• Train, optimize and deploy object detection models on edge hardware (AXIS ACAP)
• Build and maintain the containerized software stack on Linux, managed through GitOps workflows (Git as source of truth, automated build/test/deploy)
• Collaborate with controls engineers to translate detection events into machine signals (e.g., stop a machine when a person is too close) and validate end-to-end behavior
• Monitor model performance in the field, collect edge cases and iterate on datasets and models, with growing autonomy over the engagement

Minimum Requirements

• Bachelor's degree (completed or final year) in Computer Science, Computer Engineering, Electrical Engineering, or a related field
• Strong foundation in programming fundamentals; proficiency in Python, working knowledge of C/C++
• Solid Linux background (command line, services, logs, networking basics)
• Hands-on experience with Docker (images, Compose, volumes, container troubleshooting)
• Familiarity and experience with Git-based CI/CD and GitOps practices
• Coursework or project experience in computer vision / deep learning
• Willingness to travel regularly to client manufacturing sites

Preferred Qualifications

• Experience with CV/DL frameworks (PyTorch, TensorFlow, OpenCV) and object detection models (YOLO, SSD, or similar)
• Experience optimizing models for edge devices (TFLite, ONNX, quantization) on hardware such as AXIS ACAP, NVIDIA Jetson, Coral or Raspberry Pi
• Basic understanding of industrial controls (PLCs, digital I/O, Modbus, OPC-UA) and machine safety concepts
• Experience with IP cameras, video streaming (RTSP) or network configuration
• Prior internship, research, or hands-on hardware/IoT project experience
• Strong communication skills; comfort working directly with client engineering teams