ML/AI Engineer

RAPSYS TECHNOLOGIES PTE LTD; Singapore; Vor Ort; SGD 120.000 - 180.000; Vollzeit

Zusammenfassung

RAPSYS TECHNOLOGIES PTE LTD in Singapore invites an experienced ML/AI Engineer to design end-to-end agentic AI solutions, build robust ML pipelines and integrate with enterprise data platforms. You will shape architecture choices, implement governance, and collaborate with data scientists and engineers to deliver production-grade AI systems.

The role emphasizes hands-on development of LLM-enabled or multi-agent workflows, strong Python engineering, and strong problem-solving to translate

Qualifikationen

• Minimum 3–5 years building and deploying ML/AI apps, incl. at least one LLM-enabled system.
• Experience with multi-agent or tool-use frameworks is preferred.

Aufgaben

• Design end-to-end architecture for agentic AI solutions, incl. orchestration and tool integration.
• Develop AI agents to analyse information sources for fraud and risk signals.
• Integrate AI workflows with data platforms (Redshift, Glue, PySpark) and enterprise APIs.
• Implement CI/CD, testing, and governance gates for AI components.
• Collaborate with data scientists and business users to operationalise AI solutions.
• Produce maintainable code and technical runbooks.

Kenntnisse

Python

APIs

Testing

Version Control

LLM Apps

Workflow Orchestration

Data Integration

Governance

Ausbildung

Bachelor's degree in Computer Science or related

3–5 years hands-on ML/AI experience

Tools

LangGraph

CrewAI

PySpark

Databricks

Docker

GitHub Actions

Jobbeschreibung

Role: ML/AI Engineer

Jd

Working Hours Mon–Fri, 8:30am–6:00pm

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 FAM 2.0 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

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.

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 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.