Associate AI Engineer (Banking Sector)

NTT SINGAPORE PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

NTT SINGAPORE PTE. LTD. seeks an AI/ML Engineer with Data Science and GenerativeAI capabilities to design and deliver next-generation AI solutions across Global Financial Markets.

You will design agentic AI platforms, retrieval systems, and predictive models, collaborating with business stakeholders, data scientists, and software engineers to translate challenges into scalable AI-powered solutions.

Qualifications

• Hands-on experience building and deploying ML or AI solutions into production.
• Proficient in Python, SQL, and REST APIs.
• Experience with Scikit-Learn, XGBoost/LightGBM, TensorFlow or PyTorch.

Responsibilities

• 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 production-ready AI services and APIs.
• Integrate AI solutions with enterprise systems and data sources.
• Monitor model performance and continuously improve accuracy and relevance.

Skills

Machine Learning Engineering

Data Science

AI Engineering

Advanced Analytics

Python

SQL

REST APIs

Tools

Scikit-Learn

XGBoost

LightGBM

TensorFlow

PyTorch

Docker

Kubernetes

CI/CD

Git

Job description

AI/ML Engineer (Agentic AI & Data Science)

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

About the Role

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

• 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.