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Software Engineer (Arkansas & Surrounding States) at Maneva. About Maneva . Maneva, a startup founded by an ex-Google Deepmind researcher, is an AI service provider revolutionizing manufacturing operations with cutting-edge AI solutions for autonomous factory operation and optimization. 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. . 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 . . Company Location: United States.