AI Engineer, Agentic AI for Fraud Detection | Contract

Job ResponsibilitiesDesign 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 reviewDevelop 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 logicIntegrate 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 workloadsConvert prototypes into modular, tested, maintainable and deployable services, workflows, APIs or agentic applications, with clear interfaces, version control and operational ownershipBuild automated workflows for testing, validation and promotion of code, prompts, workflows and AI components across environments, with quality, security and human approval gates where requiredImplement 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 injectionImplement observability, dashboards and alerts covering system health, latency, throughput, errors, resource utilisation, data quality, workflow performance and operating costsSupport incident management, troubleshooting and root-cause analysisWork closely with data scientists, business users and analysts to capture domain expertise, refine agent workflows and ensure solutions address operational fraud detection needsProduce maintainable code, architecture decision records, evaluation evidence, operational runbooks and technical documentation, and conduct knowledge transfer to support the long-term sustainability of the solutionJob RequirementsDegree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Systems or a related technical discipline3 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 useStrong Python software engineering capability, including API development, testing, version control and modular system designExperience with LLM applications, retrieval, tool use, workflow orchestration or multi-agent frameworks such as LangGraph, CrewAI or equivalentExperience integrating AI/ML solutions with enterprise data platforms, databases, APIs or cloud servicesAbility to design and implement systematic evaluation frameworks for AI systems, including testing, benchmarking, observability and reproducibilityWorking knowledge of AI governance, security and Responsible AI practices, including human-in-the-loop controls, access management, auditability and safeguards against misuseStrong problem-solving and analytical skills, with the ability to translate business requirements into scalable AI-enabled solutionsStrong engineering judgement and structured experimentation, with the ability to distinguish a robust MVP from a demonstrationAble to explain architecture, evaluation evidence, risks and controls to technical, governance and management audiencesCollaborative working style with fraud-domain officers, designing systems that capture their expertise while leaving decisions and accountability with themAble to integrate quickly into an existing team and work effectively with limited supervisionComfortable working across research, engineering, integration, documentation and stakeholder engagementExperience with AWS services such as Bedrock, Redshift, Glue, S3 or Lambda will be an advantageExperience with PySpark, Databricks or large-scale data processing and feature-engineering pipelines will be an advantageExperience with CI/CD, containerisation, MLOps or model deployment tooling (e.g. Docker, MLflow, GitHub Actions, Jenkins) will be an advantageExperience implementing monitoring, observability and operational support processes for ML/AI applications will be an advantageExperience delivering fraud detection, risk, compliance, investigation or decision-support solutions will be an advantageExperience with knowledge retrieval, RAG, graph-based workflows, prompt management, model evaluation tooling or AI observability platforms will be an advantageFamiliarity with Singapore Government or public-sector technology environments, including data governance, security and compliance requirements, will be an advantageExperience working with cross-functional teams of data scientists, engineers, analysts and business stakeholders will be an advantageInterested candidates are invited to apply via the “Apply Now” button.We regret that only shortlisted candidates will be notified.By submitting your personal data and CV, you consent to Nexbridge Recruitment Pte Ltd collecting, using and disclosing your personal data for recruitment purposes.Nexbridge Recruitment Pte LtdEA License No: 26C3616EA Personnel: Ching Ning XinRegistration No: R22108651