AI/ML Engineer(Agentic AI & Data Science)

Ridik Pte. Ltd.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Ridik Pte. Ltd. in Singapore seeks an AI/ML Engineer to design and deliver next-generation AI solutions across Global Financial Markets, focusing on Agentic AI platforms, RAG, and predictive models.

You will collaborate with business stakeholders, data scientists, and engineers to translate challenges into scalable AI-powered solutions, with emphasis on performance, scalability, and cost efficiency.

Qualifications

• Must have hands-on experience building and deploying ML/AI solutions into production.
• Strong background in ML engineering, data science, and AI governance practices.

Responsibilities

• Design and deliver AI solutions for Global Financial Markets across multiple use cases.
• Develop agentic AI platforms, RAG solutions, and orchestration workflows.
• Build production-ready AI services and APIs and integrate with enterprise systems.
• Collaborate with stakeholders, data scientists, and software engineers to translate business challenges into scalable solutions.

Skills

Python

SQL

REST APIs

ML in prod

Data Science

AI Engineering

Advanced Analytics

Tools

TensorFlow/PyTorch

Scikit-Learn

XGBoost/LightGBM

Docker

Kubernetes

CI/CD

OpenShift

Job description

We are seeking an AI/ML Engineer with strong Data Science and Generative AI capabilities to design and deliver next-generation AI solutions across Global Financial Markets.

The role will focus on developing Agentic AI platforms, recommendation engines, retrieval systems, and predictive models supporting initiatives such as market intelligence, product recommendations, client engagement, workflow automation, and knowledge management.

You will work closely with business stakeholders, product owners, data scientists, and software engineers to translate business challenges into scalable AI-powered solutions.

Key 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:
• Client propensity prediction
• Recommendation systems
• Classification and ranking
• Behavioral analytics
• 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:
• Machine Learning Engineering
• Data Science
• AI Engineering
• 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.