AI Engineer, Agentic AI for Fraud Detection | Contract

Job Responsibilities • 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 the Platform Team) • Develop AI agents that analyse approved information sources to identify, generate and prioritise evidence-linked emerging fraud and abuse risks for officer review • 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 • Integrate agent workflows with FAM 2.0 data and analytics components, including Redshift, Glue and PySpark, and build and maintain the data preparation and feature-engineering processes needed for AI and ML workloads • Convert prototypes into modular, tested, maintainable and deployable services, workflows, APIs or agentic applications, with clear interfaces, version control and operational ownership • Build automated workflows for testing, validation and promotion of code, prompts, workflows and AI components across environments, with quality, security and human approval gates where required • 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 • 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 • Work closely with data scientists, business users and analysts to capture domain expertise, refine agent workflows and ensure solutions address operational fraud detection needs • Produce maintainable code, architecture decision records, evaluation evidence, operational runbooks and technical documentation, and conduct knowledge transfer to support the long-term sustainability of the solution Job Requirements • Degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Systems or a related technical discipline • 3 years of hands-on experience building and deploying ML/AI applications, including at least one LLM-enabled or agentic AI system delivered beyond prototype into operational use • Strong Python software engineering capability, including API development, testing, version control and modular system design • 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 • Strong engineering judgement and structured experimentation, with the ability to distinguish a robust MVP from a demonstration • Able to explain architecture, evaluation evidence, risks and controls to technical, governance and management audiences • Collaborative working style with fraud-domain officers, designing systems that capture their expertise while leaving decisions and accountability with them • Able to integrate quickly into an existing team and work effectively with limited supervision • Comfortable working across research, engineering, integration, documentation and stakeholder engagement • Experience with AWS services such as Bedrock, Redshift, Glue, S3 or Lambda will be an advantage • Experience with PySpark, Databricks or large-scale data processing and feature-engineering pipelines will be an advantage • Experience with CI/CD, containerisation, MLOps or model deployment tooling (e.g. Docker, MLflow, GitHub Actions, Jenkins) will be an advantage • Experience implementing monitoring, observability and operational support processes for ML/AI applications will be an advantage • Experience delivering fraud detection, risk, compliance, investigation or decision-support solutions will be an advantage • Experience with knowledge retrieval, RAG, graph-based workflows, prompt management, model evaluation tooling or AI observability platforms will be an advantage • Familiarity with Singapore Government or public-sector technology environments, including data governance, security and compliance requirements, will be an advantage • Experience working with cross-functional teams of data scientists, engineers, analysts and business stakeholders will be an advantage We regret that only shortlisted candidates will be notified. #J-18808-Ljbffr