Senior Software Engineer

Key Responsibilities

Design and Build Scalable Data Infrastructure: Develop backend systems and services that collect, process, and manage large volumes of sensor, equipment data and manufacturing system data from industrial environments.

Develop Full-Stack Applications: Create user-friendly applications and dashboards using Angular (or similar), paired with modern backend frameworks such as .NET, Node.js, or Python.

Integrate Real-Time Systems: Implement data pipelines that process streaming data (e.g., Kafka, Spark Streaming) for time-sensitive applications such as anomaly detection or predictive analytics.

Data Modeling: Design and implement comprehensive data models that optimize data organization, relationships, and access patterns across various data stores while ensuring scalability, performance, and alignment with manufacturing requirements and analytical needs.

Enable AI Deployment: Work closely with data scientists and engineers to support deployment, monitoring, and scaling of AI/ML models in production.

Support Cloud-Native Architecture: Build and operate containerised microservices using Docker and Kubernetes on cloud platforms such as AWS, Azure, or Cloudera.

Manage DevOps and MLOps Pipelines: Set up automated deployment workflows and manage the lifecycle of both applications and machine learning models.

Collaborate Across Disciplines: Partner with manufacturing experts, researchers, and software teams to turn industrial challenges into robust technical solutions.

Requirements

Degree in Computer Science, Software Engineering, or a related technical field.

Minimum of 5 years in software development or backend infrastructure roles.

Proficiency in one or more backend frameworks (e.g., C#/.NET, Node.js, Python, Go). Experience with C#/.NET is a plus.

Strong hands-on experience with Docker, Kubernetes, and public cloud platforms (AWS, Azure, or Cloudera).

Solid understanding of DevOps practices, including CI/CD and version control.

Exposure to MLOps workflows, machine learning lifecycle tools, or data science model integration.

Experience working with real-time data systems, data modeling, message queues, or industrial protocols is a strong advantage.

Familiarity with manufacturing environments or industrial automation systems is a plus.

Excellent problem-solving, communication, and team collaboration skills.

Good to Have

Working with streaming architectures (e.g. Kafka).

Building data pipelines for time series or sensor data.

Integrating systems with industrial IoT protocols (MQTT, OPC UA, SCADA).

Experience in AI-driven applications such as predictive maintenance or visual inspection

Work Location : West of Singapore