ML/AI Engineer (Fraud Analytics Model) - Government Project

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

Job summary

NTT Singapore Pte. Ltd. is seeking an experienced ML/AI Engineer embedded in the Data Analytics and AI Division (DSAD) to design and build two agentic AI systems for FAM2.0.

The role focuses on surfacing emerging fraud risks and translating validated risks into actionable detection logic, with human validation and responsible-AI guardrails throughout. The engineer will deliver MVPs, build in-house capability, and collaborate with DSAD data scientists, business users and analysts to ensure

Qualifications

• Degree in Computer Science, AI, Data Science, Software Engineering or related field.
• 3–5 years hands-on ML/AI app development and deployment, incl. at least one LLMed system.
• Strong Python, API development, testing and modular design.
• Experience with LLM apps, retrieval, tools, workflows or multi-agent frameworks.
• Experience with enterprise data platforms, APIs or cloud services.
• Knowledge of AI governance, security and Responsible AI practices.

Responsibilities

• Design end-to-end architecture for FAM agentic AI solutions and orchestration.
• Develop agents to identify emerging fraud risks and translate risks into detection logic.
• Integrate agent workflows with data platforms (Redshift, Glue, PySpark).
• Turn prototypes into modular, deployable services and APIs.
• Build CI/CD pipelines for testing, validation and promotion of AI components.
• Implement evaluation frameworks, governance controls and guardrails.
• Establish observability, dashboards, alerts, and reliability measures.
• Collaborate with DSAD data scientists and business users to capture domain knowledge.
• Produce technical runbooks and conduct knowledge transfer.

Skills

Python software engineering

LLM-enabled systems

Agentic AI

API development

CI/CD

Data integration

Problem solving

Education

Bachelor’s degree in CS/AI/Data Science/Software Engineering

Tools

LangGraph

CrewAI

PySpark

Databricks

AWS (Bedrock, Redshift, Glue)

Job description

Role Overview

The ML/AI Engineer is embedded within Data Analytics and AI Division (DSAD), working as part of the division's Fraud Analytics Model (FAM) team. The role supports FAM2.0, which detects misuse of public training subsidies, by designing and building two agentic AI systems. The first surfaces emerging or blind-spot fraud risks. The second converts validated risks into actionable detection logic. Human validation, evaluation and responsible-AI guardrails are embedded throughout. The engineer is expected to deliver working, evaluated MVPs and to build up DSAD's in-house capability to operate and maintain these systems independently.

Key Responsibilities

• Agentic AI Architecture: Design the end-to-end architecture for FAM agentic AI solutions, including agent orchestration, workflow design, state management, tool integration, human approval points and system interactions, using suitable frameworks such as LangGraph or CrewAI. Architecture decisions are subject to consultation with DSAD's Platform Team.
• Emerging-Risk Agent Development: Develop AI agents that analyse approved information sources to identify, generate and prioritise evidence-linked emerging fraud and abuse risks for officer review.
• Detection Engineering Agent Development: Develop AI agents that assess data feasibility, propose candidate risk indicators and detection features, and support the translation of validated risks into actionable analytics and detection logic.
• Data & Analytics Integration: Integrate agent workflows with FAM2.0 data and analytics components, including Redshift, Glue and PySpark. Build and maintain data preparation and feature-engineering processes required to support AI and ML workloads.
• Production Engineering: Convert prototypes into modular, tested, maintainable and deployable services, workflows, APIs or agentic applications, with clear interfaces, version control and operational ownership.
• CI/CD & Automation: Build automated workflows for testing, validation and promotion of code, prompts, workflows and AI components across environments, incorporating quality, security and human approval gates where required.
• Evaluation & AI Guardrails: Implement evaluation frameworks and governance controls covering output quality, reproducibility, traceability, access control, exception handling, human approval gates and safeguards against untrusted inputs such as prompt injection.
• Monitoring & Reliability: Implement observability, dashboards and alerts covering system health, latency, throughput, errors, resource utilisation, data quality, workflow performance and operating costs. Support incident management, troubleshooting and root-cause analysis.
• Domain Collaboration: Work closely with DSAD data scientists, business users and analysts to capture domain expertise, refine agent workflows and ensure solutions address operational fraud detection needs.
• Documentation & Knowledge Transfer: Produce maintainable code, architecture decision records, evaluation evidence, operational runbooks and technical documentation, and conduct knowledge transfer to support long-term sustainability of the solution.

Requirements & Qualifications

A. Educational Qualification & Experience

Education: Degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Systems or a related technical discipline.

Experience: 3-5 years of hands-on experience building and deploying ML/AI applications. This must include at least one LLM-enabled system delivered beyond prototype into operational use.

B. Technical & Professional Competencies

• Strong Python software engineering capability, including API development, testing, version control and modular system design.
• Hands-on experience building and deploying ML/AI applications, including at least one LLM-enabled or agentic AI solution beyond proof-of-concept stage.
• Experience with LLM applications, retrieval, tool use, workflow orchestration or multi-agent frameworks such as LangGraph, CrewAI or equivalent.
• Experience integrating AI/ML solutions with enterprise data platforms, databases, APIs or cloud services.
• Ability to design and implement systematic evaluation frameworks for AI systems, including testing, benchmarking, observability and reproducibility.
• Working knowledge of AI governance, security and Responsible AI practices, including human-in-the-loop controls, access management, auditability and safeguards against misuse.
• Strong problem-solving and analytical skills, with the ability to translate business requirements into scalable AI-enabled solutions.

Preferred (Advantageous)

• Experience with AWS services such as Bedrock, Redshift, Glue, S3, Lambda or related cloud-native services.
• Experience with PySpark, Databricks or large-scale data processing and feature engineering pipelines.
• Experience with CI/CD, containerisation, MLOps or model deployment tooling (e.g. Docker, MLflow, GitHub Actio