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Company Overview
A leading bank in Asia with a global network of over 500 branches and offices across 19 countries and territories, this organization provides comprehensive banking and financial services. With more than 80 years of history, the bank operates through head offices and subsidiaries in key Asian markets and maintains a focus on integrity, enterprise, unity, and long-term commitment. The organization emphasizes secure, fair, and well-governed use of AI across its operations, aligning with industry best practices and regulatory expectations.
Key Responsibilities
Establish bank-wide independent validation requirements for AI/ML/GenAI models and agentic systems across the three lines of defense
Validate high materiality models and AI use cases, including GenAI and Agentic AI
Develop and implement enterprise standards for fairness, robustness, reliability, explainability, cybersecurity, and responsible AI
Operationalize MAS FEAT principles, MAS TRM, Outsourcing/Cloud Guidelines, PDPA obligations, and internal Model Risk Management practices
Conduct deep reviews of data lineage, features, architectures, and metrics; challenge design choices and approve/remediate high-risk models
Review AI red teaming assessments including prompt injection/jailbreaks, data poisoning, market/behavioral stress scenarios, and failure mode analysis
Enforce documentation, transparency artifacts, audit trails, and production monitoring for drift, fairness, safety, and abuse; drive periodic revalidation
Partner with business, data science, engineering, compliance, legal, cyber, operations, internal audit, and brief senior management and board risk committees
Interface with regulatory bodies and industry groups
Manage and provide strategic direction to a team of AI risk specialists
Education Requirements
University graduate in Computer Science, Data Science, Statistics, Applied Mathematics, Electrical/Computer Engineering, or related quantitative field
Certifications in AI ethics/responsible AI, model risk management, cybersecurity, privacy (PDPA), or governance preferred
Job Requirements
10–15 years of experience in AI, responsible AI, or AI & data governance, preferably in banking or financial services
Mastery of statistical inference, hypothesis testing, experimental design, power analysis, resampling, and Bayesian methods
Hands-on expertise in supervised/unsupervised learning, ensembles/gradient boosting, and neural architectures (CNNs, RNNs, Transformers)
Proficiency in regularization, feature selection, score calibration, reject inference, and interpretability across tabular, time-series, text, and graph data
Experience with LLM fine-tuning (LoRA/PEFT), instruction tuning, RLHF/RLAIF, retrieval augmented generation, prompt design, and hardening
Ability to build evaluation harnesses for truthfulness, grounding, toxicity, bias, jailbreak resistance, hallucinations, latency, and cost, and enforce production guardrails
Validate agent workflows with tool use, planning/critique loops, escalation rules, and human-in-the-loop checkpoints
Analyze autonomy levels, error propagation, and recovery patterns, and design safe execution policies
Participate in red-team exercises for prompt injection/jailbreaks, data poisoning, evasion, membership inference, and model extraction
Apply fairness metrics and FEAT-aligned impact assessments; produce model cards, fairness reports, and decision traceability artifacts
Design secure prompts/models; implement output validation, watermarking/traceability, and tool execution guardrails; integrate with enterprise controls
Conduct AI system threat modeling; align with SOC procedures, incident response, and secure SDLC
Translate policy into control requirements, KPIs/KRIs, validation checklists, and audit artifacts; prepare board/regulator reporting
Proficient in Python, SQL, PySpark, PyTorch/TensorFlow; LLM orchestration with LangChain/LlamaIndex; vector databases
Familiarity with Cloud (AWS/GCP/Azure), Kubernetes, containerization, secure secrets management, API governance, rate-limiting, and content filtering
Disclaimer
SpringCube curates tech job listings from various company websites to support tech professionals in Singapore.
1. No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality. 2. No Client Relationship: This company is not a client of SpringCube unless stated. 3. To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job. 4. No Liability: SpringCube is not liable for inaccuracies.
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