Work with clients and senior teammates to analyze business challenges, map workflows (service blueprints) to better understand customer journey in order to identify gaps and opportunities for AI/ML use cases.
Engineer traceability and integrity into the platform: citations, transformation lineage from source document to chunk, content hashing and tamper evidence, and an append-only audit of every access decision and write
Good English reading, writing, and oral communication skills. General English proficiency requirements for PhD admission: TOEFL >=90, IELTS >=6.5 (English proficiency requirements may be relaxed for exceptionally outstanding applicants)
Our AI Transformation team is building an internal platform that helps our people use AI effectively and develop software with greater speed and confidence. Initiatives include an internal agentic AI work platform for creating and running workflows across internal tools, AI-driven SDLC automation to help engineers plan, build, test, and ship software, and a centralised knowledge base that gives people and agents reliable access to company context.
PhD degree required, with top artificial intelligence conference papers (NeurIPS, ICML, ICLR, CVPR, ACL, OSDI, NSDI, SC and SigMOD, etc.) in machine learning (ML), computer vision (CV), natural language processing (NLP) and other fields.
Support the planning and execution of AI and Advanced Industry (Life & Health, advanced manufacture, etc.) project deliverables. Assist in monitoring project scope, schedule, budget, quality, risks, and issues to ensure alignment with business objectives. Contribute to agile team activities to deliver quality outputs.
Build and scale a PB-scale data lake / lakehouse for multimodal spatial data — object storage layout, partitioning, open table formats, catalogs, and metadata
Hands-on Technical Leadership: Stay technically involved and step in when needed to write code, debug issues, review architecture and resolve complex engineering problems.
Lead the development of Generative AI applications leveraging Large Language Models (LLMs) for use cases such as intelligent search, document understanding, virtual assistants, content generation, and knowledge management.
AI Service Enablement: Enable enterprise-wide adoption of AI capabilities by developing reusable APIs, SDKs, templates and shared services that simplify AI integration and accelerate solution delivery across teams, while maintaining AI security and compliance within the organisation.