About Us

Our solution generates AI-powered actions and insights using off-the-shelf hardware or existing vision systems for real-impact manufacturing problems in products and equipment inspection, production efficiency, safety, and more. 

Requirements

Role & Responsibilities

• On-Premise Infrastructure Architecture: Design and implement robust software infrastructure for deploying vision-based AI applications directly on manufacturing floor devices and edge computing platforms.
• Production Software Development: Build and maintain production-grade software applications on Linux-based edge devices, including AI inference pipelines, image processing workflows, and system monitoring solutions.
• Reliable Operations Management: Implement comprehensive monitoring, logging, alerting, and error recovery systems to ensure high availability and reliability of deployed AI systems in industrial manufacturing environments.
• Vision System Integration: Develop software interfaces for AI vision systems addressing manufacturing quality control, productivity optimization, safety monitoring, and equipment uptime challenges.
• Data Platform Development: Contribute to building AI-powered platforms that provide data analysis for connected facility operations, including data collection, processing, and analytics pipelines.
• IoT & Fleet Management: Build and support device management systems for on-premise AI deployments, including remote monitoring, configuration management, and fleet-wide software orchestration across manufacturing sites.
• OTA Deployment Systems: Design and implement over-the-air software update mechanisms for distributed on-premise devices, ensuring safe and reliable remote updates with minimal production disruption.
• Industrial Integration: Collaborate with hardware teams to integrate AI applications with PLCs, existing industrial automation infrastructure, and manufacturing execution systems.
• Performance Optimization: Profile and optimize software performance for resource-constrained edge environments and real-time processing requirements in manufacturing settings.

 

Must-Have

• Strong proficiency in Python for production software development and system architecture
• Proven experience architecting and building successful infrastructure solutions that ensure uptime and reliability of real-time on-premise applications
• 3–5 years of experience in building production-grade software systems, preferably for industrial or manufacturing environments
• Cloud computing experience with major platforms (AWS, Azure, GCP) for hybrid edge-cloud deployments and infrastructure management
• Hands-on experience with Linux systems, command line operations, and system administration for edge computing platforms
• Experience with containerization technologies (Docker) and deployment of applications in production environments
• Understanding of computer vision workflows and AI inference pipelines for manufacturing applications
• Knowledge of application reliability principles: monitoring, alerting, graceful degradation, error recovery, and system health management
• Understanding of manufacturing environments and challenges related to quality control, productivity, safety, and equipment uptime
• Strong debugging and problem-solving skills in production environments with minimal downtime tolerance

 

Strongly Preferred

• Full-stack web development experience with TypeScript and React for building operator interfaces and dashboards
• Experience with IoT protocols and device management for industrial environments (MQTT, HTTP/REST APIs, industrial networking)
• Experience with over-the-air (OTA) software deployment and update mechanisms for on-premise industrial devices
• Experience with NVIDIA Jetson or similar edge computing platforms for AI deployment in manufacturing
• Knowledge of industrial automation protocols (Modbus, Ethernet/IP, OPC-UA) and PLC integration

 

Nice To Have

• Experience with time-series databases and analytics platforms for manufacturing data (InfluxDB, Grafana, Prometheus)
• Background in computer vision libraries (OpenCV) and machine learning frameworks (TensorFlow, PyTorch) deployment
• Familiarity with manufacturing execution systems (MES) and quality management systems
• Experience with device management platforms for industrial IoT deployments
• Understanding of cybersecurity best practices for on-premise industrial systems
• Knowledge of data pipeline architectures for connected facility analytics
• Experience in food & beverage, CPG, automotive, or packaging manufacturing environments

 

Preferred Candidate Profile

• On-Premise Deployment Experience: Candidates who have deployed and maintained software systems directly in industrial/manufacturing environments, addressing network constraints, security requirements, and uptime expectations
• Production Reliability Background: Experience in production systems where downtime has direct business impact (manufacturing, industrial automation, critical infrastructure)
• Vision/AI Application Deployment: Experience deploying computer vision or AI applications in real-world production environments, with an understanding of model performance, data quality, and system integration challenges
• Manufacturing Domain Knowledge: Understanding of manufacturing processes, quality control requirements, and operational constraints in production environments
• Infrastructure Mindset: Candidates who prioritize system architecture, scalability, monitoring, and long-term maintenance—not just feature development
• Edge Computing Experience: Familiarity with resource-constrained environments, edge device management, and distributed system challenges in industrial settings