Job Description

Define and execute the end-to-end AI strategy: opportunity selection, model development, secure MLOps, AI governance, and client delivery. Build scalable, compliant, and trustworthy AI systems for consulting engagements and internal products.

Key responsibilities

Own AI strategy and portfolio: identify high-ROI use cases, size value, and set the platform roadmap. Lead cross-functional AI teams spanning research, engineering, MLOps, evaluation, and model risk. Establish an AI management system aligned to ISO/IEC 42001, including policies, risk controls, and continuous improvement. Implement AI governance and testing practices aligned to Singapore's AI Verify and Model AI Governance Framework for Generative AI, including safety, robustness, bias, and toxicity testing. Ensure PDPA-compliant data lifecycle management, privacy-by-design, and data minimization across training and inference. Build secure MLOps: model registry, lineage, monitoring, rollback, and vulnerability management for AI assets. Define model evaluation protocols and acceptance criteria per use case, including human-in-the-loop. For financial-sector clients, align development and deployment with MAS FEAT principles and the Veritas toolkit. Drive client delivery: scoping, solution architecture, delivery oversight, and pre-sales support. Build partnerships with hyperscalers, universities, and model providers. Publish internal standards for prompt engineering, RAG patterns, safety rails, and evaluation.

Requirements

8 to 12+ years across data science and ML engineering, 4+ years in AI leadership. Production experience with LLMs and classical ML, vector stores, RAG, and guardrails. Demonstrated setup of MLOps on cloud platforms and secure deployment patterns. * Working knowledge of Singapore AI governance, PDPA, and international AI assurance standards.