Company Overview

Red Dot AI is a Singapore-based deep-tech company spun off from Nanyang Technological University (NTU). We build AI-powered digital twin and operational intelligence platforms for data centres and mission-critical infrastructure, helping operators improve efficiency, resilience, sustainability and decision-making.

Role Summary

We are looking for a hands-on Full Stack Engineer / Software Engineer to develop, deliver and continuously improve our platform across frontend, backend, APIs, data workflows and project delivery. This role sits close to product engineering and real-world implementation: you will build scalable product features, support platform delivery for customer projects, and contribute to demos, PoCs and engineering workflows.

AI-assisted engineering is an important part of how we work. You will be expected to use modern AI coding tools and agentic coding workflows to improve development speed, code quality, testing, documentation and delivery. AI is not the core focus of the role; it is a practical engineering capability used to build better software faster and more safely.

Key Responsibilities

1. Full-Stack Platform Development: Design, develop and maintain features across frontend, backend and API layers for Red Dot AI’s data centre intelligence platform. Build robust, maintainable and scalable software for dashboards, workflow modules, data-driven applications and operational tools.
2. Frontend Engineering: Develop modern, responsive user interfaces using React, Next.js and TypeScript. Translate product requirements and design concepts into clean, usable and reliable user experiences for engineering, operations and customer-facing workflows.
3. Backend & API Development: Build backend services, RESTful APIs, business logic, data models and integration layers using Python and modern web frameworks such as FastAPI, Django or Flask. Ensure backend services are reliable, testable and easy to operate.
4. Platform Delivery & Project Implementation: Support the delivery of platform features into real customer or project environments. Work with project, product and engineering teams to handle environment-specific requirements, configuration, deployment readiness, data access and integration needs.
5. Data & System Integration: Develop connectors, API integrations, data ingestion workflows and basic processing logic for systems such as BMS, DCIM, sensors, telemetry platforms, databases, files and other project-specific sources. Supported interfaces may include RESTful APIs, MQTT, Modbus, OPC and other protocols depending on project needs.
6. AI-Assisted Engineering & Workflow Improvement: Use AI coding tools such as Claude Code, Codex, OpenCode, Cursor, GitHub Copilot or similar tools to accelerate coding, refactoring, testing, debugging and documentation. Help define practical AI-assisted development workflows, review patterns and guardrails for reliable engineering output.
7. Agentic Coding & Harness Engineering: Where appropriate, create lightweight harnesses for validating AI-generated or agent-assisted work, including unit tests, integration tests, API test harnesses, data validation scripts, mock services, regression checks and deployment verification scripts.
8. PoCs, Demos & Innovation Prototypes: Build internal demos and proof-of-concepts to validate new product ideas, emerging technologies and customer scenarios. Convert research ideas and field insights into working prototypes that can inform product direction.
9. Collaboration & Technical Ownership: Work closely with product managers, data scientists, project engineers and domain experts. For senior candidates, contribute to technical design, code review, mentoring, delivery planning and engineering standards.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering or a related field.
• 2+ years of relevant experience for the Software Engineer role; 5+ years with strong technical ownership for the Senior Software Engineer role.
• Strong proficiency in Python and experience with backend web frameworks such as FastAPI, Django or Flask.
• Solid frontend development experience with React, Next.js and TypeScript.
• Experience designing and consuming RESTful APIs, working with service-oriented architecture and building production-grade application logic.
• Experience with relational databases such as PostgreSQL and familiarity with NoSQL or time-series data stores.
• Familiarity with Docker and containerized application development; Kubernetes experience is preferred for senior candidates.
• Understanding of SDLC, Agile delivery, CI/CD practices, testing and code review workflows.
• Practical experience using AI coding tools or strong willingness to adopt AI-assisted engineering workflows.
• Strong problem-solving ability, clear communication and the ability to work independently in a cross-functional team.

Preferred Qualifications

• Hands-on experience with Kubernetes, Helm, cloud deployment or container-based delivery.
• Experience with cloud platforms such as AWS, GCP or Azure.
• Experience with time-series databases such as InfluxDB, data processing pipelines, telemetry data or monitoring systems.
• Experience integrating with industrial, IoT or infrastructure systems, including BMS, DCIM, sensors, MQTT, Modbus, OPC or RESTful APIs.
• Experience developing internal tools, workflow automation, testing harnesses or developer productivity tooling.
• Familiarity with agentic coding, AI coding agents, prompt-driven development workflows, code generation review practices or AI-assisted test generation.
• Basic understanding of data centres, HVAC, cooling systems, energy optimisation or operational technology is a plus.
• Passion for emerging technology, innovation and applying new tools pragmatically to improve engineering outcomes.

Ideal Candidate Profile

The ideal candidate is a practical full-stack engineer who can move across frontend, backend, data integration and project delivery. You care about product quality and implementation reality: not only whether the code works locally, but whether it can be tested, deployed, maintained and used in real project environments.

You are comfortable using AI coding tools as part of your daily workflow, but you remain disciplined about review, testing and reliability. You are curious about emerging technologies, passionate about innovation, and willing to learn data centre and infrastructure domain knowledge to build software that solves real operational problems.

What Success Looks Like

• Core platform features are delivered with clean frontend interfaces, reliable backend services and clear APIs.
• Project delivery becomes smoother because software is easier to configure, test, deploy and troubleshoot.
• AI-assisted workflows improve engineering productivity without compromising code quality, security or maintainability.
• Reusable components, test harnesses and internal tools reduce repeated manual work across product and project teams.
• The engineering team benefits from clearer documentation, stronger testing practices and better cross-functional collaboration.