AI Senior Digital Engineer

This position is responsible for bridging the gap between central AI software and physical industrial automation infrastructure. You are responsible for defining and harmonizing scalable system architectures that align with enterprise strategies and technology standards. Your mission is to deliver adaptable, high-performance architectures by seamlessly blending cloud and edge-based AI models with physical Operational Technology (OT). You contribute to architectural consistency across product lines, develop forward-looking system roadmaps, and define strict requirements that ensure data reliability, edge scalability, and robust cybersecurity.

Acting as the primary technical interface between Data Science teams and business units, you manage relationships with external system integrators to validate predictive analytics, drive innovation, and optimize energy efficiency.

• Integration Architecture: Define, maintain, and execute the integration architecture between Edge System Management, AI Hub services, and existing physical equipment.
• Pipeline Interoperability: Design and optimize end-to-end data pipelines ensuring seamless data flow between legacy hardware and modern cloud/edge AI environments.
• Model Lifecycle Management: Build and maintain scalable systems for containerized AI model distribution, runtime integration, and live deployment at the edge.

• Target Architecture: Define and communicate the architectural frameworks, ensuring strict technical consistency across existing platform infrastructure.
• Data Schema Design: Translate technical requirements into low-level integration designs, covering API contracts, system interfaces, data schemas, and integration patterns.
• Architectural Governance: Drive core technical decisions and ensure strict alignment on tech stacks across distributed engineering teams.

• Hands-on Guidance: Provide deep technical support to development teams through strict code/design reviews, technical guidance, and problem-solving.
• System Integration: Systematically diagnose and resolve engineering mismatches, integration bottlenecks, and connectivity friction between cloud software and hardware.
• Validation & Testing: Lead structural validation, edge scalability testing, and end-to-end system behavior checks across all integrated components.

• Cross-Team Engineering: Act as the primary technical bridge between Data Science/AI teams, Edge Architecture groups, and core Engineering.
• Standards Enforcement: Drive the adoption of strict engineering best practices regarding scalability, observability, performance, data reliability, and cybersecurity.
• Tech Exploration: Conduct targeted Proof of Concepts (PoCs) specifically focused on validating new integration paths, orchestration tools.

• Education: Bachelor’s or master’s degree in computer science, Electrical Engineering, Data Science, Industrial Automation, or a related technical field is required.
• Experience: 6+ years in software engineering or industrial automation, with a proven track record of technically leading complex, full-scale systems integration projects.
• System Delivery: Deep hands-on experience building, operating, and maintaining distributed systems that span Cloud + Edge + IoT Devices.
• Technical Influence: Demonstrated leadership skills with the ability to guide, motivate, and align cross-functional engineering teams on complex technical architectures without direct reporting authority.

• AI-Enabled Integration: Advanced knowledge of machine learning life cycles, edge-to-cloud model deployment, API integration with AI services, and data pipeline plumbing.
• Industrial Automation & OT Connectivity: High proficiency in blending AI models with physical OT infrastructure, including SCADA systems, PLCs, and network protocols.
• Dev Ops & Observability: Solid command of CI/CD infrastructure, automated integration testing, and deep system observability tools across networks.
• Ecosystem Familiarity: Exposure to or proficiency with desired…