ML/AI Engineer - Fraud Analytics Model (Public Sector)
First seen by Alion on Oct 28, 2026.
- 1-year contract, renewable
- Government project
- Hybrid work arrangement
Role Overview
The ML/AI Engineer will join the Fraud Analytics Model (FAM) team within the Data Analytics and AI Division (DSAD). The role supports FAM 2.0, which detects misuse of public training subsidies, by designing and building two agentic AI systems.
One system will surface emerging fraud risks and blind spots. The other will help turn validated risks into actionable detection logic. Human validation, evaluation and responsible AI guardrails will be built into both systems.
You will deliver working, evaluated minimum viable products (MVPs) and help build DSAD’s in-house capability to operate and maintain them independently.
Key Responsibilities
Agentic AI architecture: Design the end-to-end architecture for FAM agentic AI solutions, including agent orchestration, workflows, state management, tool integration, human approval points and system interactions. Use suitable frameworks such as LangGraph or CrewAI. Architecture decisions will be made in 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 and analytics integration: Integrate agent workflows with FAM 2.0 data and analytics components, including Redshift, Glue and PySpark. Build and maintain the data preparation and feature engineering processes needed to support AI and ML workloads.
Production engineering: Turn prototypes into modular, tested, maintainable and deployable services, workflows, APIs or agentic applications, with clear interfaces, version control and operational ownership.
CI/CD and automation: Build automated workflows to test, validate and promote code, prompts, workflows and AI components across environments. Include quality, security and human approval gates where required.
Evaluation and AI guardrails: Implement evaluation frameworks and governance controls covering output quality, reproducibility, traceability, access control, exception handling, human approval and safeguards against untrusted inputs, such as prompt injection.
Monitoring and reliability: Implement observability, dashboards and alerts for system health, latency, throughput, errors, resource use, 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 and knowledge transfer: Produce maintainable code, architecture decision records, evaluation evidence, operational runbooks and technical documentation. Conduct knowledge transfer to support the long-term sustainability of the solutions.
Requirements and Qualifications
Education and ExperienceEducation: 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, including at least one LLM-enabled system delivered beyond the prototype stage into operational use.
Strong Python software engineering skills, 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 the 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.
Experience with AWS services such as Bedrock, Redshift, Glue, S3, Lambda or related cloud-native services.
Experience with PySpark, Databricks, large-scale data processing or feature engineering pipelines.
Experience with CI/CD, containerisation, MLOps or model deployment tools such as Docker, MLflow, GitHub Actions or Jenkins.
Experience implementing monitoring, observability and operational support processes for ML/AI applications.
Experience delivering fraud-detection, risk, compliance, investigation or decision-support solutions.
Experience with knowledge retrieval, retrieval-augmented generation (RAG), graph-based workflows, prompt management, model evaluation tools or AI observability platforms.
Familiarity with Singapore Government or public-sector technology environments, including data governance, security and compliance requirements.
Experience working with cross-functional teams of data scientists, engineers, analysts and business stakeholders.
Strong engineering judgement and a structured approach to experimentation, with the ability to distinguish a robust MVP from a demonstration.
Ability to explain architecture, evaluation evidence, risks and controls to technical, governance and management audiences.
Collaborative approach to working with fraud-domain officers. You will design systems that capture their expertise while keeping decisions and accountability with them.
Ability to integrate quickly into an existing team and work effectively with limited supervision.
Comfort working across research, engineering, integration, documentation and stakeholder engagement.
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Skills
API Development, Multi-Agent Systems, Fraud Investigation Planning and Execution, Agent Orchestration, Technical Skills, LangGraph, Risk Monitoring, Data Solutions, Artificial Intelligence, Platform Architecture, Computer Science, Version Control Systems, Fraud Analytics, Cloud Services, User Acceptance Testing, Education, CrewAI