AVP, AI Engineer, Global Financial Market Technology, Group Technology
LocationSingapore - Central
Typefull-time
SeniorityVP
DepartmentData Technology
Company size10,000+ people
First seenOct 10, 2026 · 1d ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
Must have6
Hands-on experience in AI/ML Engineering, Data Science, or Advanced Analytics, including deploying ML/AI solutions into production
Strong proficiency in Python, SQL, and REST APIs
Experience with ML frameworks such as Scikit-Learn, XGBoost/LightGBM, TensorFlow, or PyTorch
Mandatory Generative AI experience covering RAG, Vector Search, LLM applications, Dify, and Agentic AI frameworks
Knowledge of data pipelines, data transformation, feature engineering, and data quality management
Experience with OpenShift (OCP), Docker, Kubernetes, CI/CD, and Git
Nice to have2
Preferred experience in banking, financial services, capital markets, or wealth management
Advantageous experience in recommendation/personalization engines, search and retrieval, knowledge management, MLOps/LLMOps, AI governance, and enterprise document repositories
Skills
Python
SQL
REST APIs
Scikit-Learn
XGBoost
LightGBM
TensorFlow
PyTorch
RAG
Vector Search
LLM
Dify
OpenShift
Docker
Kubernetes
CI/CD
Git
Business Function
Group Technology enables and empowers the bank with an efficient, nimble and resilient infrastructure through a strategic focus on productivity, quality & control, technology, people capability and innovation. In Group Technology, we manage the majority of the Bank’s operational processes and inspire to delight our business partners through our multiple banking delivery channels.
Responsibilities
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Design and develop Agentic AI, Generative AI, and RAG solutions, including AI agents, orchestration, tool calling, prompt engineering, memory, reflection, and evaluation.
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Build ML and Data Science models for client propensity, recommendations, classification, ranking, behavioral analytics, and next-best-action use cases.
•
Analyze structured and unstructured data, performing data exploration, feature engineering, model evaluation, and performance monitoring.
•
Develop scalable, production-ready AI services and APIs and integrate them with enterprise systems and data sources.
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Build reusable AI components and frameworks that can support multiple business use cases.
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Implement monitoring, observability, and evaluation capabilities while optimizing AI solutions for scalability, performance, accuracy, and cost.
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Partner with business stakeholders, product owners, data scientists, and engineers to translate Global Financial Markets requirements into AI-powered solutions.
Requirements
•
Hands-on experience in AI/ML Engineering, Data Science, or Advanced Analytics, including deploying ML/AI solutions into production.
•
Strong proficiency in Python, SQL, and REST APIs.
•
Experience with ML frameworks such as Scikit-Learn, XGBoost/LightGBM, TensorFlow, or PyTorch.
•
Mandatory Generative AI experience covering RAG, Vector Search, LLM applications, Dify, and Agentic AI frameworks.
•
Knowledge of data pipelines, data transformation, feature engineering, and data quality management.
•
Experience with OpenShift (OCP), Docker, Kubernetes, CI/CD, and Git.
•
Preferred experience in banking, financial services, capital markets, or wealth management.
•
Advantageous experience in recommendation/personalization engines, search and retrieval, knowledge management, MLOps/LLMOps, AI governance, and enterprise document repositories.