- Islandwide (Singapore) Singapore
Working Location
Job Description
Responsibilities
Responsibilities
Generative AI & Agentic AI
• Design and develop AI agents using modern agent frameworks.
• Build and optimize RAG (Retrieval-Augmented Generation) solutions.
• Develop agent orchestration workflows and tool-calling frameworks.
• Implement prompt engineering, evaluation, reflection, and memory capabilities.
• Build reusable AI components that can be leveraged across multiple business use cases.
Machine Learning & Data Science
• Develop machine learning models for:
o Client propensity prediction
o Recommendation systems
o Classification and ranking
o Behavioral analytics
o Next-best-action recommendations
• Perform data exploration, feature engineering, and model evaluation.
• Analyze large structured and unstructured datasets to generate actionable insights.
• Monitor model performance and continuously improve accuracy and relevance.
AI Application Development
• Build production-ready AI services and APIs.
• Integrate AI solutions with enterprise systems and data sources.
• Implement monitoring, observability, and evaluation frameworks.
• Optimize AI solutions for performance, scalability, and cost efficiency.
Required Qualifications Experience
• Experience in:
o Machine Learning Engineering
o Data Science
o AI Engineering
o Advanced Analytics
• Hands-on experience building and deploying ML or AI solutions into production.
Technical Skills Programming
• Python (mandatory)
• SQL
• REST APIs
Machine Learning Experience with:
• Scikit-Learn
• XGBoost / LightGBM
• TensorFlow or PyTorch
Generative AI – (MANDATORY)
Experience in all the following areas:
• RAG
• Vector Search
• LLM Applications
• Dify
• Agentic AI frameworks
Data Engineering Knowledge of:
• Data pipelines
• Data transformation
• Feature engineering
• Data quality management
Cloud & DevOps Experience with:
• OCP (Openshift Platform)
• Docker
• Kubernetes
• CI/CD pipelines
• Git
Preferred Qualifications
• Experience with financial services, banking, capital markets, or wealth management.
• Experience building recommendation engines or personalization solutions.
• Experience with search, retrieval, and knowledge management platforms.
• Familiarity with MLOps, LLMOps, and AI governance practices.
• Experience working with unstructured document repositories and enterprise knowledge sources.
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