IIoT and Industrial AI Solution Architect

We are seeking a highly authoritative Solution Architect to lead end-to-end Industry 4.0, IIoT, and Industrial AI deployments for enterprise infrastructure clients. This is a dual-faceted role requiring a rare combination of executive-level technical consulting and deep, hands-on engineering execution. The successful candidate will define the strategic OT/IT convergence roadmap while actively building, deploying, and troubleshooting complex containerized edge workloads, secure network topologies, and edge-native machine learning pipelines.

Strategic Consulting & Client Leadership

• Customer-Facing Authority: Act as the primary technical anchor for enterprise clients, translating complex, high-stakes industrial requirements into scalable, secure, and highly available edge-to-cloud architectures.
• AI & IoT Roadmapping: Drive the technical strategy for competitive bids and RFIs/RFPs, delivering proof-of-concepts (PoCs) that showcase the convergence of IoT telemetry with generative and predictive AI.
• Cross-Functional Orchestration: Lead and mentor global engineering teams, bridging the gap between offshore edge developers and on-site operational technology (OT) engineers.

Industrial AI & Machine Learning Integration (Good to have)

• Generative AI & RAG Architectures: Design and deploy Retrieval-Augmented Generation (RAG) pipelines to contextualize Large Language Models (LLMs) with proprietary OT data, maintenance manuals, and historian logs for intelligent operator assistance and automated root-cause analysis.
• Edge AI & MLOps: Operationalize lightweight machine learning models as containerized Azure IoT Edge modules to execute real-time anomaly detection, predictive maintenance, and closed-loop control tasks directly at the asset level.
• Advanced Telemetry Analytics: Architect the data pathways required to feed raw edge telemetry into enterprise AI platforms, optimizing Overall Equipment Effectiveness (OEE) and driving autonomous operational workflows.

Advanced Edge & Embedded Engineering

• Hands-On Edge Deployment: Architect, provision, and troubleshoot custom Azure IoT Edge runtimes on hardened industrial gateways.
• OS & Container Mastery: Deep practical expertise in Linux environments (specifically configuring and securing distributions for industrial use) and orchestrating containerized workloads (Docker/Moby) for real-time edge processing.
• Complex Sensor Integration: Direct the physical and logical integration of critical infrastructure telemetry, including DGA sensors, SF6 gas analyzers, and environmental stations, handling complex data payload parsing.

Cloud Architecture & Data Orchestration

• Azure IIoT Ecosystem: Advanced design and implementation of Azure IoT Hub, Device Provisioning Service (DPS) at scale, Event Hubs, and Azure Functions.
• Data Topologies: Build highly resilient, real-time ingestion pipelines feeding into ADX, Data lake, and cloud-native AI/ML workspaces.
• Digital Twin Modeling: Formulate and deploy Azure Digital Twins concepts to map complex physical operations to digital, event-driven architectures.

Industrial Protocols & Secure Networking

• Protocol Translation: Deep, hands-on proficiency in acquiring and translating legacy and modern industrial protocols, including Modbus TCP/RTU, OPC-UA, BACnet, MQTT/AMQP, and configuring LoRaWAN networks for challenging RF environments.
• Advanced Networking: Design and implement robust edge network connectivity, including configuring IPSec VPNs, OpenVPN, and routing topologies over cellular and dedicated APNs.
• Zero-Trust IIoT Security: Implement stringent edge-to-cloud security governance, managing MQTTS/TLS 1.2+ pipelines, X.509 certificate lifecycles, and secure device provisioning protocols.

Qualifications & Experience

• 12-15+ years of progressive corporate experience spanning embedded systems engineering, IT/OT architecture, and enterprise IIoT consulting.
• Demonstrable track record of successfully delivering full-lifecycle digital factory or critical infrastructure projects (ISA-95 frameworks).
• Proven experience operationalizing AI/ML workloads within industrial or edge-constrained environments.
• Active, relevant certifications in Azure architecture (AZ-104, AZ-900, or equivalent IoT/AI specialties) are highly preferred.