Job Purpose
The Technical Lead will drive the architectural evolution, technical strategy, and engineering execution of Singapore’s Maritime Digital Twin (MDT). As the platform transitions into a next-generation Maritime Geospatial Reasoning Platform, the incumbent will lead a high-performing technical squad to deliver real-time, high-fidelity data aggregation, predictive layers, and deep-tech AI integration. The role sits at the intersection of cutting-edge technology and national maritime infrastructure, transforming raw industry data into mission-critical operational intelligence.
Key Responsibilities
A. System Architecture & Technical Leadership
• Platform Evolution: Formulate and execute the technical roadmap to scale the MDT data architecture, transitioning from a data aggregation platform to an intelligent, predictive geospatial reasoning platform.
• Data Fusion & Streaming: Design and implement robust architectures capable of ingestion, normalization, and low-latency processing of massive, multi-sensor maritime data streams.
• Technical Governance: Serve as the final technical authority for the platform, ensuring code quality, scalable design patterns, and rigorous system documentation.
B. Advanced Technology & AI Integration
• Predictive Layer Development: Oversee the deployment of advanced AI modules for complex environmental and hydrodynamic simulations
• LLM Workflows: Integrate Multi-modal Large Language Models (LLMs) and automated workflows to assist in decision-support tooling for port operations.
C. Ecosystem Development & API Standardization
• Drive the development of a secure, "Plug-and-Play" common data infrastructure framework and AI Sandbox.
• Design standardized communication protocols (MQTT, AMQP, RESTful APIs) and developer playbooks to allow research partners, institutions, and maritime startups to securely build capabilities on top of the MDT infrastructure.
D. Security, Compliance & Infrastructure
• Oversee infrastructure security and deployment lifecycles by partnering with MPA and government-wide cybersecurity teams to execute robust hardening strategies, including regular penetration testing and vulnerability assessments.
• Manage containerized deployments across hybrid cloud environments utilizing Docker, Kubernetes, and automated CI/CD pipelines to ensure continuous delivery and operational resilience.
E. Stakeholder & Squad Management
• Lead and mentor a group of multi-domain specialised engineers. Collaborate closely with key internal and external ecosystem stakeholders to implement prioritised use cases
JOB REQUIREMENTS
• Background in Computer Science, Software Engineering, Data Engineering, Artificial Intelligence, or a closely related technical field. Background in Geospatial Science or Machine Learning is highly advantageous.
• Software Engineering: Minimum of 8-10 years of experience in technical leadership, systems design architecture, or leading core engineering teams for enterprise-grade distributed systems.
• Data Engineering: Proven track record in building complex data pipelines, multi-sensor data fusion networks, and real-time streaming architectures (e.g., Kafka, Flink, Spark).
• AI/ML Expertise: Strong hands-on experience deploying machine learning models in production environments. Deep familiarity with GeoAI, spatial reasoning tools (GIS, ArcGIS), or PhysicsAI frameworks is highly desirable.
• Protocols & DevOps: Deep proficiency in containerization (Docker, Kubernetes) and standardized messaging protocols (REST, MQTT, WebSockets, AMQP).
• Strategic Agility: Ability to balance immediate technical delivery with the long-term 2031 strategic vision of public-sector infrastructure commercialization.
• Influence & Collaboration: Excellent communication skills to bridge the gap between deep-tech engineering and high-level strategic/regulatory requirements.
• Problem-Solving: Strong entrepreneurial mindset with a knack for navigating ambiguous environments and unblocking critical technical dependencies.
• Experience with Unity or similar real-time 3D development environments is advantageous. Familiarity with concepts such as scene management, asset optimisation, or simulation workflows will help you better support the team and engage meaningfully with technical decisions around the visualisation and modelling components of the platform.
Successful candidates will be offered a 2-year contract in the first instance and may be considered for an extension or be placed on a permanent appointment
