AI Architect

Who we are: Do you want to join our Geo-data revolution? Fugro’s global reach and unique know-how will put the world at your fingertips. Our love of exploration and technical expertise help us to provide our clients with invaluable insights. We source and make sense of the most relevant Geo-data for their needs, so they can design, build and operate their assets more safely, sustainably and efficiently. But we’re always looking for new talent to take the next step with us. For bright minds who enjoy meaningful work and want to push our pioneering spirit further. For individuals who can take the initiative but work well within a team. Job Summary: The AI Architect is the technical authority responsible for designing secure, scalable and cost-efficient AI architectures for APAC deployments. The role defines technical standards, patterns and guardrails across models, data pipelines, APIs, cloud infrastructure and security layers, ensuring solutions align with global governance while meeting APAC data sovereignty and regulatory requirements. The role provides technical direction to the team, supporting the design, build, maintenance and continuous improvement of advanced AI and automation solutions. You will design and maintain the architecture for the AI enabled Marine Ground Digital Twin Platform. This is a 3-year fixed term contract for placement in Singapore, and only open to SG/PR candidates. Key Responsibilities: 1. Architecture Design & Standards • Design scalable, secure and cost-efficient AI system architectures for regional deployments. • Define and enforce technical standards, design patterns and architectural guardrails for the team. • Lead build vs buy vs integrate decisions, evaluating platforms and vendors. • Produce architecture artefacts (solution designs, data flows, integration maps, technical roadmaps). 2. Cloud & Infrastructure • Architect solutions across relevant cloud platforms (Azure, AWS and compliant options as required). • Design for multi-cloud and hybrid environments where regulations demand. • Ensure infrastructure meets data sovereignty requirements across APAC countries. • Review cloud cost architecture to balance performance and spend. • Ensure AI development and deployment environments are designed for reliability, reproducibility, access control and operational support. 3. AI Platform & Model Strategy • Designs and governs AI and machine learning environments, including patterns for experimentation, deployment, monitoring and operational support. • Evaluate and select foundational models, fine-tuning approaches and RAG architectures appropriate to each use case. • Design MLOps and LLMOps pipelines (model versioning, evaluation, monitoring, retraining triggers). • Define the regional AI platform strategy (shared services, reusable components, integration patterns). • Stay current on model developments and assess architectural implications for the roadmap. 4. Security & Governance • Embed security controls (encryption, access controls, audit logging) into all architectural decisions. • Align architecture to global governance standards and partner with security and risk stakeholders. • Conduct technical risk assessments for new AI initiatives and define mitigation architecture. Requirements: • Education: Bachelor’s or Master’s degree (IT, Computer Science, Engineering, Data/AI, or equivalent experience) • Working experience: 8+ years - Experience in solution, software, data or enterprise architecture • Experience using data platforms, data lakes, lakehouses, or data warehouses to discover, analyse, and prepare governed datasets for advanced analytics, machine learning, and AI use cases. • Proficiency in SQL and Python for data access, analysis, and preparation. • Ability to work effectively with large datasets, apply data quality and governance standards, and collaborate with business and technical stakeholders to translate business challenges into data-driven solutions. • Understanding of security-by-design, privacy-by-design, data governance, and access control Nice-to-have: • Understanding of AI solution patterns; RAG, model integration, agentic workflows, and secure enterprise deployment • Microsoft Fabric, Databricks, Azure data services or comparable lakehouse/data platforms • MLOps, LLMOps, model lifecycle management or AI evaluation frameworks • API design, DevOps or infrastructure-as-code • Vector databases, semantic search, fine-tuning or generative AI evaluation • Power Plat…