Senior ML/AI Engineer

ML/AI Engineer (Senior Consultant)

Key Responsibilities

· Agentic AI Architecture: Design the end-to-end architecture for 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.

· Emerging-Risk Agent Development: Develop AI agents that analyse approved information sourcesto identify, generate and prioritise evidence-linked emerging fraud and abuse risks for officer review.

· Detection Engineering Agent Development: Develop AI agents that assess datafeasibility, 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 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 approvalgates where required.

· Evaluation & AI Guardrails: Implement evaluation frameworks and governance controls covering output quality, reproducibility, traceability, accesscontrol, 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 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 beyondproof-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 Actions, 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, RAG, graph-based workflows, prompt management, model evaluation tooling 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 comprising datascientists, engineers, analysts and business stakeholders.

C. Soft Skills & Communication Attributes

· Strong engineering judgement and structured 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.

· Ability to integrate quickly into an existing team and work effectively with limited supervision.

· Comfort working across research, engineering, integration, documentation and stakeholder engagement.