About Berge Bulk
Situated within Singapore's vibrant maritime hub, Berge Bulk leads innovation in the shipping industry. Embracing their vision of leading the world towards a zero carbon future through safe, efficient, and sustainable shipping practices, Berge Bulk is making significant strides in technological advancements. With a goal to achieve carbon neutrality by 2025, Berge Bulk is actively shaping the future of shipping. This strategic pursuit reflects their commitment to maritime technology leadership.
Berge Bulk’s three key milestones
Berge Bulk has set an ambitious decarbonization timeline which consists of three key milestones…
- Offset 100% of Scope-1 carbon emissions from 2025.
- Build and operate a zero-emissions vessel by 2030.
- Achieve zero-emissions fleetwide by 2050.
Download our Brochure to find out more about Berge Bulk
Purpose of the Job
The AI Platform Engineer will develop and operate the “Digital Workbench” –and handle the deployment, integration, and ongoing MLOps (Machine Learning Operations) of AI models and services in production.
The successful candidate will be a self‑starter who combines strategic insight with meticulous execution. You must be comfortable juggling a matrix of relationships—including external vendors, internal system owners, senior business leaders, functional heads, and end‑users—and be able to translate their diverse needs into clear, actionable plans. A proven track record of advising on AI‑enabled solutions, championing continuous improvement, and setting realistic expectations is essential.
Job Accountabilities/Key Responsibilities
- Custom App Development: Develop and maintain the “Digital Workbench” – a custom Azure application serving as an MCP client (mission control interface) that communicates with the Fabric MCP Server to leverage enterprise data and AI models
- MLOps & Deployment: Implement machine learning pipeline integration, model deployment, and monitoring. Ensure ML models and AI features (e.g. Copilot/LLM-based services) are reliably deployed, scaled, and monitored in production
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Data Integration: Connect the Digital Workbench to Fabric IQ and other data sources, using Fabric’s public APIs and MCP to fetch semantic context and serve AI-driven insights to user
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Platform Maintenance: Oversee Azure infrastructure, implement CI/CD for AI services, manage versioning of data schemas and models, and ensure system security and performance
Qualifications & Experience
- Software & Cloud Engineering: Strong software development skills (preferably in Azure environment; knowledge of Azure services like App Services, Functions, AI/ML services). Proficiency in programming for cloud-based applications (e.g. Python, C#/.NET, or similar
- MLOps & DevOps: Experience with machine learning operations –model deployment, CI/CD, containerization (Docker/Kubernetes), monitoring, and logging for AI applications
- Data & API Integration: Familiarity with RESTful APIs, data pipelines, and possibly GraphQL; understanding of Microsoft Fabric components (Lakehouse, pipelines, etc.) and how to use MCP clients/servers for data access
- Collaboration & Problem-Solving: 5+ years in software or data engineering roles; ability to work closely with architects, data scientists, and business teams to implement AI solutions and troubleshoot production issues
