AI/ML Engineer (Agentic AI & Data Science)

IDC TECHNOLOGIES (SINGAPORE) PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

IDC Technologies (Singapore) is seeking an AI/ML Engineer to design and deliver next-generation AI solutions across Global Financial Markets. You will focus on generative and agentic AI platforms, including RAG, vector search, and LLM applications, with production-ready services and APIs for enterprise systems.

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

Qualifications

• Hands-on experience building and deploying ML/AI solutions into production.
• Experience in ML engineering, data science, AI engineering or advanced analytics.

Responsibilities

• Design and develop AI agents and agent orchestration workflows.
• Build Retrieval-Augmented Generation (RAG) solutions and tool-calling frameworks.
• Develop production-ready AI services and APIs for enterprise use.
• Analyze data pipelines and feature engineering to improve models.
• Monitor model performance and optimize for accuracy, scale and cost.

Skills

Python

SQL

REST APIs

Scikit-Learn

XGBoost

LightGBM

TensorFlow

PyTorch

RAG

Vector Search

LLM Applications

Dify

Agentic AI

Data pipelines

Feature engineering

CI/CD

Git

Kubernetes

Docker

OpenShift

Tools

Docker

Kubernetes

OpenShift

CI/CD tooling

Git

Job description

About the Role

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

• 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.

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.