Job description

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 About Reply Reply specializes in the design and implementation of solutions based on new communication channels and digital media. Reply is a network of highly specialized companies supporting global industrial groups operating in the telecom and media, industry and services, banking, insurance and public administration sectors in the definition and development of business models enabled for the new paradigms of AI, cloud computing, digital media and the Internet of Things. Reply services include Consulting, System Integration and Digital Services. The base compensation range for this full-time position is between $100,000-$120,000 plus benefits. Compensation decisions are supported through market data, where regional variances may exist based on cost of labor. We also consider prior experience, relevant skills, education, training, certifications, and other job-related qualifications. If you have questions regarding compensation, the talent acquisition team can provide details during the interview process. Reply is an equal opportunity employer. We are committed to provide equal opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. If you need assistance and reasonable accommodation due to a disability during the application or the recruiting process, email us at jobusa@reply.com. Visit our website at www.reply.com to learn more about our open roles.

Requirements

Must-haves

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
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

Nice-to-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipythondockerci/cd

Platform: lever
Effort: quick

Benefits

Benefits detected in the listing.

Vision