全职 AI Engineer-Insurance Decision Intelligence | Contract 工作, 薪水, TG Malaysia Federal Territory 公司招聘中 - Ricebowl

Company Overview

Our client is a leading regional insurance and takaful provider with a strong presence across ASEAN markets. Backed by an established financial services group and a global insurance partner, the organization offers a wide range of life insurance, general insurance, family takaful, and general takaful solutions to individuals, businesses, and corporate clients.

About the Role

We are looking for an AI Engineer to develop and deploy machine learning and AI

solutions that support data-driven decision making across the insurance business. You

will work on projects involving underwriting, fraud detection, claims processing,

customer analytics, and document intelligence.

This is a great opportunity for candidates with experience in machine learning,

predictive analytics, or AI engineering who are interested in solving real-world business

challenges at scale.

Key Responsibilities

• Develop and deploy machine learning models for underwriting, claims analytics,

and fraud detection.

• Perform data analysis, feature engineering, and model optimization using large

datasets.

• Build AI solutions for extracting information from insurance-related documents.

• Support the full AI model lifecycle, from development and testing to production

deployment and monitoring.

• Collaborate with business stakeholders to identify opportunities for AI-driven

improvements.

• Contribute to AI governance, standards, and best practices.

Requirements

• 3 to 5 years of experience in AI, Machine Learning, Data Science, or Predictive

Analytics.

• Junior candidates with relevant project experience are encouraged to apply.

• Strong Python programming skills, including Pandas and NumPy.

• Experience with machine learning frameworks such as Scikit-learn, PyTorch, or

TensorFlow.

• Knowledge of predictive modeling techniques such as XGBoost, LightGBM,

Random Forest, Ensemble Models, or Anomaly Detection.• Experience deploying machine learning models into production environments.

• Understanding of model monitoring, version control, and retraining processes.

• Experience with explainable AI techniques such as SHAP or LIME is an

advantage.

Preferred Experience

• Exposure to insurance, banking, fintech, or risk analytics domains.

• Experience with fraud detection, risk scoring, customer analytics, or decision

support systems.

• Familiarity with document intelligence solutions, OCR, computer vision, or LLM

based document processing.

• Knowledge of RAG (Retrieval-Augmented Generation) and enterprise knowledge

retrieval solutions.

Example Projects

• Insurance fraud detection and risk prediction.

• Underwriting risk scoring models.

• Claims and policy document automation.

• Customer segmentation and retention analytics.

• Detection of fraud networks and abnormal claim patterns