Senior Platform Engineer

About RDA

Red Dot AI is a deep-tech company incubated by Nanyang Technological University and accredited by IMDA located in Singapore. RDA combines physics-driven digital twins, AI agents, and real-time infrastructure intelligence to help data-centre and critical-infrastructure customers turn complex operational data into actionable decisions across compute, energy, cooling, and operations.

Role Overview

This role brings RDA's software and AI products from development, cloud, and test environments into reliable customer delivery, while keeping field devices, industrial data, edge systems, and cloud services working together. The scope combines DevOps, Edge Integration, Field Engineering, and application-layer development where required.

This is a hands-on engineering role rather than a pure cloud-operations, site-implementation, or project-management position. The engineer will contribute directly to architecture decisions, code and scripting, deployment, data integration, troubleshooting, and delivery documentation while coordinating clear responsibilities and handovers across product, software, AI, project, customer, and vendor teams.

Core Responsibilities

1. Cloud Platform, Deployment, and Operations

• Maintain Azure, GCP, Alibaba Cloud, or comparable cloud environments together with Linux, virtual machines, Docker, Kubernetes, and related runtime foundations for development, testing, demonstrations, and customer projects.
• Build and maintain CI/CD, container registries, configuration and secrets management, certificates, logging, monitoring, backup and recovery, and release processes.
• Turn environment setup, deployment, upgrades, patching, and rollback into repeatable scripts, templates, checklists, and runbooks.
• Participate in production issue diagnosis and recovery, and work with the team to improve observability, capacity management, reliability, and operational efficiency.

2. Industrial IoT and Edge Data Integration

• Integrate with BMS, DCIM, SCADA, IoT gateways, device-control systems, existing customer systems, and other project data sources.
• Use MQTT and, as required by the project, Modbus, OPC UA, serial interfaces, CAN, REST APIs, databases, or file interfaces to connect devices and systems.
• Design and implement protocol parsing, data mapping, interface adapters, foundational data processing, and exception handling as extensible connector or gateway components.
• Monitor data receipt, quality, timestamps, freshness, and connection health, including buffering, retry, backfill, and consistency handling for unstable networks.

3. Customer-Site and Project Delivery

• Contribute to requirements clarification, interface inventories, site-condition checks, technical solutions, implementation planning, risk identification, and delivery acceptance.
• Support installation, configuration, upgrades, incident handling, and handover in private-cloud, enterprise-network, restricted-network, or offline environments when required by a customer project; these conditions apply only to selected projects.
• Diagnose issues across software, networking, devices, data, and vendor boundaries in remote or on-site environments, establish ownership, and drive issues to closure.
• Produce deployment records, issue analyses, interface documentation, environment status, data-integration status, risk notes, and project reports.

4. Application Services and Platform Integration Development

• Develop or modify backend services, microservices, APIs, message processing, data services, and operational tools required for integration and delivery.
• Contribute hands-on development in C#/.NET, Java, Python, or comparable stacks, including REST, gRPC, messaging, caching, relational databases, and time-series data.
• Trace issues across device protocols, edge gateways, cloud services, data stores, and business applications instead of treating symptoms within a single layer.
• Work with frontend engineers on configuration, monitoring, or delivery interfaces and, where necessary, read and modify React, Angular, or Vue code.

5. Architecture Decisions and Cross-Functional Collaboration

• Make explainable engineering decisions on system boundaries, technology choices, interfaces, reliability, security, cost, and evolution paths.
• Work with product, software, AI, and project teams, as well as customer technical teams, cloud providers, and equipment vendors, to deliver agreed solutions.
• Coordinate external or partner development teams when required, including design review, code review, work breakdown, and delivery-quality review, while maintaining personal hands-on engineering involvement.
• Build practical domain knowledge in data centres, HVAC, BMS/DCIM, cooling, power, temperature and humidity, PUE, and related project contexts.

6. Engineering Automation and AI-Assisted Work

• Use Claude Code, Codex, or comparable AI engineering tools to assist with code and script development, configuration generation, log analysis, testing, and documentation.
• Build lightweight tools for deployment validation, connector testing, environment health checks, log analysis, or report generation.
• Review, test, and version-control AI-generated output so that changes remain understandable, maintainable, and reversible.

Requirements

• Bachelor's degree or above in Computer Science, Software Engineering, Automation, Control Engineering, Electronic Engineering, or a related discipline.
• At least 8 years of relevant experience across software engineering, cloud platforms, industrial IoT, systems integration, deployment engineering, or related fields, with the independent judgement and delivery ownership expected of a Senior individual contributor.
• Hands-on participation in at least one industrial IoT, connected-vehicle, smart-device, or other connected-device platform spannin