Job Description

Key Responsibilities

Lead the solution architecture for Digital Twin rollouts, including high-level and detailed design of twin models, data architecture, integration patterns, and deployment strategy.
Define end-to-end architecture covering:
Digital Twin model hierarchy and entity relationships
Real-time data ingestion and synchronization from IoT, SCADA, historians, and edge devices
State management, event processing, and simulation engines
Integration with enterprise systems (ERP, MES, PLM, EAM)
Analytics, and visualization layers
Select and design the optimal technology stack for scalable digital twin deployments, including in-memory computing platforms (e.g., ScaleTwin/ScaleOut), cloud services (AWS, Azure), streaming platforms (Kafka, MQTT), and containerization (Kubernetes).
Create architecture blueprints, diagrams, and documentation (including logical, physical, and deployment views) to guide implementation teams.
Conduct architecture reviews, risk assessments, and performance/scalability evaluations for large-scale twin deployments handling millions of entities and high-velocity data.
Collaborate with Configuration Leads, Consultants, Data Engineers, and Client Stakeholders to ensure the solution is configurable, maintainable, and aligned with business requirements.
Define standards, best practices, and reusable reference architectures for future Digital Twin rollouts.
Provide technical leadership during pilot phases, full-scale rollout, go-live, and hyper-care periods.
Mentor configuration teams on digital twin architecture principles.

Required Qualifications & Experience

10+ years of overall IT/solution architecture experience, with at least 4–6 years in Digital Twin, IIoT, IoT platforms, or real-time simulation solutions.
Proven track record in successfully architecting and rolling out large-scale Digital Twin solutions for manufacturing, logistics, energy, or asset-intensive industries.
Strong expertise in:
Digital Twin platforms and frameworks (ScaleTwin, ScaleOut Digital Twins, Azure Digital Twins, NVIDIA Omniverse, or equivalent)
Real-time data architectures (Kafka, MQTT, OPC UA, Spark Streaming)
Cloud platforms (AWS IoT, Azure IoT Hub, Google Cloud)
In-memory computing, complex event processing, and stateful applications
Microservices, containerization (Docker, Kubernetes), and DevOps practices
Bachelor’s or Master’s degree in Computer Science, Electrical/Mechanical/Industrial Engineering, or related field.