At Rite-Hite, your work makes an impact. As the global leader in loading dock and door equipment, we design and deliver solutions that keep our customers safe, secure, and productive. Here, you'll find innovation, stability, and the chance to grow your career as part of a team that's always looking ahead. Rite-Hite is a global leader in industrial safety and efficiency solutions. As part of our digital transformation journey, we are seeking a Systems Architect, Edge AI/ML to help build and scale intelligent systems that deliver real-time insights, automation support, and customer value at the edge of industrial environments. The Systems Architect, Edge AI/ML is responsible for defining and guiding the architecture for artificial intelligence and machine learning deployed on devices and edge platforms. This role ensures that AI/ML solutions are scalable, reliable, secure, and aligned with enterprise product and platform objectives. The architect partners closely with product management, device and edge software teams, data and cloud teams, and digital solution leaders to translate business and customer requirements into robust AI/ML foundations that enable consistent behavior, efficient lifecycle management, and high-quality user experiences across industrial environments. CORE RESPONSIBILITIES Edge AI/ML Architecture Strategy Define and maintain reference architectures, design patterns, and standards for deploying AI and ML models on edge devices and platforms. Establish common approaches for data ingestion, feature pipelines, model inference, performance optimization, and lifecycle management at the edge. Ensure architectural decisions support real-time operation, scalability, reliability, safety, and security. Innovation Leadership Drive the strategic use of AI and ML at the edge by establishing repeatable patterns for model development, deployment, monitoring, and update. Own a portion of the internal technology radar related to AI/ML, edge analytics, and sensing technologies. Foster a culture of responsible, ethical, and transparent AI practices aligned with cybersecurity, privacy, and safety requirements. Product and Platform Enablement Partner with product management and engineering teams to enable AI-driven capabilities such as condition monitoring, vision-based sensing, anomaly detection, and predictive insights. Guide architectural decisions related to edge compute constraints, sensor integration, and real-time inference workloads. Promote reuse and consistency of AI/ML components across products and platforms. Integration and Interoperability Define patterns for integration between edge AI/ML solutions, device software, hybrid mobile applications, and enterprise or cloud-based platforms. Ensure solutions support online, offline, and hybrid deployment models common in industrial environments. Promote interoperability with internal systems and approved third-party technologies. Security, Safety, and Quality Define secure-by-design and safety-aligned principles for AI/ML model deployment and operation at the edge. Ensure alignment with applicable safety, quality, and industrial cybersecurity standards. Participate in architecture and design reviews to assess risk, resilience, and compliance. Technical Leadership and Cross-Functional Collaboration Collaborate across product management, device and edge software, data science, cloud platforms, manufacturing, service, and mobile application teams. Provide technical leadership, mentorship, and guidance to engineers and teams working with AI/ML technologies. Serve as a technical advisor in strategic customer conversations and enterprise implementations when appropriate. Supervisory Responsibilities This role does not have direct reports but is expected to provide technical leadership and architectural guidance across multiple teams. QUALIFICATIONS Education Master's degree preferred. 8+ years of experience delivering AI/ML-enabled solutions, with demonstrated experience deploying models in edge or resource-constrained environments. Experience with industrial, connected, or safety-critical systems preferred. Knowledge / Skill Requirements Strong understanding of AI/ML architectures, model lifecycle management, and real-time inference at the edge. Experience designing systems that balance performance, accuracy, latency, and resource constraints. Ability to think systemically across sensors, devices, edge platforms, mobile applications, and cloud services. Strong communication skills and ability to influence cross-functional stakeholders. Familiarity with industrial automation, IoT ecosystems, or computer vision is a plus. Leadership
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