Job Title

Company DescriptionRecurrent Software leads the way in leveraging the transformative potential of Generative AI and Machine Learning across various industries. Known for innovation, the company specializes in developing advanced solutions to tackle complex challenges. By combining deep domain expertise with cutting-edge technical capabilities, Recurrent Software delivers tailor-made tools that enhance efficiency, optimize resource usage, and unlock new insights from data. These solutions are designed to provide measurable value and support long-term growth across diverse sectors.Role Overview We are looking for a hands-on Deployment & Remote Support Engineer to take ownership of the end-to-end installation, configuration, and commissioning of industrial vision and edge-AI systems at customer sites. The ideal candidate is equally comfortable mounting cameras and fixtures on a factory floor as they are SSH-ing into an edge device to debug a network configuration. When physical presence is not possible, they can effectively guide and coordinate on-ground technicians through remote sessions to ensure a seamless deployment. Key Responsibilities On-Site Deployment Plan, execute, and document end-to-end deployment of industrial camera systems, including IP/GigE/USB3 cameras, lens selection verification, and field-of-view validation. Physically mount cameras, lighting fixtures, brackets, and custom enclosures on production lines, conveyor systems, or facility infrastructure using appropriate fixture and mounting hardware. Install and rack-mount network switches (managed/unmanaged), patch panels, and Power-over-Ethernet (PoE) injectors as needed. Deploy, configure, and validate custom GPU-based edge computing devices, ensuring proper thermal management, power supply, and physical security of the hardware. Run and terminate Ethernet cabling (Cat5e/Cat6/fiber) between cameras, switches, and edge devices; perform cable management to industrial standards. System Configuration & Commissioning Configure IP addressing, VLANs, subnets, static routes, and firewall rules on switches and edge devices to establish a reliable and secure local network. Set up camera parameters (resolution, frame rate, exposure, triggering) and validate image quality against project specifications. Install, configure, and test application software and AI inference pipelines on edge devices; ensure models are loaded, running, and producing expected outputs. Perform end-to-end system integration testing: camera capture → network transport → edge inference → output/alerting, verifying latency and throughput targets. Configure remote access and monitoring (VPN, SSH tunnels, monitoring dashboards) for ongoing support and maintenance post-deployment. Remote Support & Coordination Serve as the primary remote deployment lead when on-site presence is not feasible; conduct video-guided installation sessions with on-ground technicians or customer staff. Prepare detailed deployment runbooks, wiring diagrams, network topology maps, and step-by-step installation guides to enable non-specialist personnel to execute physical tasks. Troubleshoot hardware and network issues remotely using diagnostic tools (ping, traceroute, SNMP, device management consoles) and guide field teams to resolution. Coordinate with cross-functional teams (engineering, product, customer success) to resolve deployment blockers and escalate issues as needed. Documentation & Continuous Improvement Maintain comprehensive deployment records including as-built diagrams, configuration backups, IP allocation sheets, and commissioning sign-off checklists. Capture field learnings and feed them back to the engineering team to improve hardware design, packaging, and deployment tooling. Develop and refine standard operating procedures (SOPs) for recurring deployment patterns. Required Qualifications 2–5 years of experience in field deployment, systems integration, or technical support for hardware/IoT/industrial systems. Hands-on experience with industrial cameras (GigE Vision, USB3 Vision, or MIPI CSI) and associated imaging software (e.g., GenICam). Practical knowledge of networking: IP addressing, subnetting, VLANs, DHCP, DNS, basic firewall rules, managed switch configuration (CLI or web GUI). Familiarity with Linux-based systems (Ubuntu/Debian): Basic command-line navigation, service management, package installation, log analysis. Experience with edge computing or embedded GPU platforms (NVIDIA Jetson, custom GPU appliances, or similar). Ability to read and create basic wiring diagrams and network topology documentation. Comfortable with physical installation work: Mounting, cable routing, and working at height or in industrial/manufacturing environments. Strong communication skills with the ability to guide non-technical field staff through complex installation steps via video or phone. Willingness to travel domestically up to 50–70% of the time. Preferred / Nice-to-Have Experience with Docker containers and container orchestration on edge devices. Familiarity with AI/ML inference frameworks (TensorRT, ONNX Runtime, OpenVINO). Knowledge of industrial protocols (Modbus, OPC-UA, MQTT) and PLC integration. Certification or training in structured cabling (e.g., BICSI) or networking (CCNA or equivalent). Experience with configuration management or remote device management tools (Ansible, Balena, Fleet management platforms). Background in computer vision or image processing fundamentals. Prior experience working in manufacturing, logistics, or warehouse environments. Tools & Technologies Hardware : Industrial cameras (GigE/USB3), GPU edge devices, PoE switches, custom fixtures & mounts Networking :Managed switches (Cisco, Netgear, TP-Link), VLANs, VPN, SSH, Wireshark, SNMP Operating Systems  : Ubuntu/Debian Linux, Windows (for camera tools) Software : Docker, Git, Python, Bash scripting, camera SDKs Remote Support : TeamViewer / AnyDesk, Zoom/Teams, remote terminal tools Documentation : Confluence, Notion, Visio/draw.io, Google Workspace What We Offer Opportunity to work at the cutting edge of industrial AI and computer vision deployment. Travel exposure to diverse industrial environments and customer sites. A collaborative team that values field experience and practical problem-solving. Competitive salary, travel allowances, and professional development opportunities. Direct impact on product development through real-world deployment feedback.