Software Architect

James Douglas Middle East; Abu Dhabi; On-site; AED 180,000 - 240,000; Full time

Job summary

James Douglas Middle East seeks a Software Architect with deep experience in industrial automation and mining digital platforms. You will shape architecture, standards, and secure patterns, while writing code alongside the team to deliver scalable, edge-enabled industrial software.

The role emphasizes integration with PLCs, SCADA, historians, cloud services, and enterprise apps, with hands-on work on MCP servers and AI-enabled copilots to support operational intelligence.

Qualifications

• 15+ years in industrial automation, mining digital platforms, or IT/OT integration.
• Strong hands-on experience in C#/.NET for scalable industrial software and APIs.
• Experience with industrial IoT platforms and microservices architecture.
• Hands-on experience with MCP servers and agentic AI frameworks; working knowledge of LLMs.

Responsibilities

• Define the solution architecture and platform standards for industrial digital platforms.
• Design scalable, secure software systems that integrate OT, cloud, and enterprise apps.
• Architect data pipelines using industrial protocols, historians, and time-series databases.
• Collaborate with stakeholders to translate requirements into robust technical solutions.
• Provide hands-on coding and mentorship to the engineering team.

Skills

C# / .NET

Industrial IoT

Microservices

LLM integration

Kafka / MQTT

Edge computing

Security & IAM

AI platforms

Platform integration

Tools

MCP servers

LangGraph

LangChain

OpenAI Agent SDK

AWS Bedrock

Azure AI

Keycloak

PostgreSQL

Graph DBs

Job description

Our client is a global mining company securing the critical minerals behind the energy transition, including copper, cobalt, nickel, tin, and rare earths; they invest in and operate premium mining assets.

They are seeking an experienced Software Architect with strong expertise in industrial automation, mining platforms, or industrial digital solutions and modern AI-enabled software architectures. The ideal candidate should have hands-on experience in designing scalable industrial software platforms that integrate with OT systems, edge devices, process historians, cloud services, and enterprise applications.

The role requires a strong understanding of industrial communication protocols, data acquisition systems, process historians, edge computing platforms, and industrial integration patterns, along with practical experience of AI, MCP servers, agentic AI frameworks, and LLM-based industrial assistants.

The Software Architect will be responsible for defining the solution architecture, technical direction, integration patterns, platform standards, and engineering best practices for industrial digital platforms. This is a hands-on role and the successful candidate will be expected to continue writing code alongside the engineering team.

Key Responsibilities

• Design and implement scalable, secure, reliable, and maintainable software systems for industrial automation and digital transformation platforms.
• Architect industrial computing and edge platforms that can operate reliably in constrained, remote, underground, or low-connectivity environments.
• Support integration with IoT devices, industrial gateways, telemetry systems, PLCs, SCADA/DCS systems, sensors, and equipment data sources.
• Define data acquisition, normalization, contextualization, storage, and data governance strategies for industrial operational data.
• Design architecture for real-time and historical data pipelines involving industrial protocols, streaming platforms, historians, relational databases, time-series databases, and graph databases.

3. AI-Enabled Industrial Applications

• Contribute to the architecture of AI-enabled industrial applications, digital assistants, copilots, and operational intelligence solutions.
• Design secure integration patterns for LLMs, MCP servers, tools, agents, knowledge bases, enterprise systems, and industrial data platforms.
• Define how industrial data, asset models, historians, graph databases, and operational systems can be safely exposed to AI agents with proper access control, traceability, and governance.

4. Technical Governance & Collaboration

• Review solution designs, architecture documents, code quality, integration approaches, platform decisions, and technology selections.
• Collaborate with product owners, business stakeholders, and customer teams to translate operational requirements into robust technical solutions.

Key Qualifications

• 15+ years' experience in industrial automation, industrial or mining digital platforms, or integration of IT/OT systems.
• Strong hands-on experience in C# / .NET for building scalable industrial software platforms, backend services, APIs, and integration services. (Mandatory)
• Experience with industrial IoT or IoT platforms, and with microservices architecture. (Mandatory)
• Hands-on experience developing MCP servers and working with agentic AI frameworks, together with a working knowledge of LLMs and how to use them. (Mandatory)
• Experience with Kafka/MQTT/RabbitMQ or similar event-streaming platforms for real-time industrial data ingestion, event processing, and system integration.
• Experience with databases such as PostgreSQL, SQL Server, time-series databases, and Graph DBs.
• Experience with platforms for modelling industrial assets, relationships, workflows, and contextual data.
• Experience with identity and access management platforms such as Keycloak, including authentication, authorization, SSO, role-based access control, and integration with enterprise identity providers.
• Experience designing or contributing to industrial AI assistants, copilots, or operational intelligence solutions.
• Knowledge of LangGraph, LangChain, OpenAI Agent SDK, AWS Bedrock, Azure AI, or similar AI platforms and frameworks.
• Understanding of knowledge graphs, semantic models, graph databases, and industrial asset models.
• Exposure to industrial use cases such as root cause analysis, production variance analysis, equipment health insights, predictive maintenance, process optimization, and operational copilots.
• Familiarity with secure AI integration patterns, including permission controls, tool access governance, auditability, and human approval workflows.