{"id":3135525,"url":"https://alion.io/job/hirevouch-senior-software-engineer-2","title":"Senior Software 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real-impact manufacturing problems in products and equipment inspection, production efficiency, safety, and more.\nRequirements\nRole & Responsibilities\nOn-Premise Infrastructure Architecture: Design and implement robust software infrastructure for deploying vision-based AI applications directly on manufacturing floor devices and edge computing platforms.\nProduction 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.\nReliable 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.\nVision System Integration: Develop software interfaces for AI vision systems addressing manufacturing quality control, productivity optimization, safety monitoring, and equipment uptime challenges.\nData Platform Development: Contribute to building AI-powered platforms that provide data analysis for connected facility operations, including data collection, processing, and analytics pipelines.\nIoT & 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.\nOTA 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.\nIndustrial Integration: Collaborate with hardware teams to integrate AI applications with PLCs, existing industrial automation infrastructure, and manufacturing execution systems.\nPerformance Optimization: Profile and optimize software performance for resource-constrained edge environments and real-time processing requirements in manufacturing settings.\nMust-Have\nStrong proficiency in Python for production software development and system architecture\nProven experience architecting and building successful infrastructure solutions that ensure uptime and reliability of real-time on-premise applications\n3-5 years of experience in building production-grade software systems, preferably for industrial or manufacturing environments\n Cloud computing experience with major platforms (AWS, Azure, GCP) for hybrid edge-cloud deployments and infrastructure management\nHands-on experience with Linux systems, command line operations, and system administration for edge computing platforms\nExperience with containerization technologies (Docker) and deployment of applications in production environments\nUnderstanding of computer vision workflows and AI inference pipelines for manufacturing applications\nKnowledge of application reliability principles: monitoring, alerting, graceful degradation, error recovery, and system health management\nUnderstanding of manufacturing environments and challenges related to quality control, productivity, safety, and equipment uptime\nStrong debugging and problem-solving skills in production environments with minimal downtime tolerance\nStrongly Preferred\nFull-stack web development experience with TypeScript and React for building operator interfaces and dashboards\nExperience with IoT protocols and device management for industrial environments (MQTT, HTTP/REST APIs, industrial networking)\nExperience with over-the-air (OTA) software deployment and update mechanisms for on-premise industrial devices\nExperience with NVIDIA Jetson or similar edge computing platforms for AI deployment in manufacturing\nKnowledge of industrial automation protocols (Modbus, Ethernet/IP, OPC-UA) and PLC integration\nNice To Have\nExperience with time-series databases and analytics platforms for manufacturing data (InfluxDB, Grafana, Prometheus)\nBackground in computer vision libraries (OpenCV) and machine learning frameworks (TensorFlow, PyTorch) deployment\nFamiliarity with manufacturing execution systems (MES) and quality management systems\nExperience with device management platforms for industrial IoT deployments\nUnderstanding of cybersecurity best practices for on-premise industrial systems\nKnowledge of data pipeline architectures for connected facility analytics\nExperience in food & beverage, CPG, automotive, or packaging manufacturing environments\nPreferred Candidate Profile\nOn-Premise Deployment Experience: Candidates who have deployed and maintained software systems directly in industrial/manufacturing environments, addressing network constraints, security requirements, and uptime expectations\n Production Reliability Background: Experience in production systems where downtime has direct business impact (manufacturing, industrial automation, critical infrastructure)\nVision/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\nManufacturing Domain Knowledge: Understanding of manufacturing processes, quality control requirements, and operational constraints in production environments\nInfrastructure Mindset: Candidates who prioritize system architecture, scalability, monitoring, and long-term maintenance-not just feature development\n Edge Computing Experience: Familiarity with resource-constrained environments, edge device management, and distributed system challenges in industrial 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