MI/AL Engineer

Rapsys Technologies Pte Ltd.; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Rapsys Technologies Pte Ltd in Singapore is seeking an MI/AI Engineer to join our team onsite. You will apply machine intelligence and AI to design and deploy real‑world solutions, working with data scientists and engineers to deliver impactful results.

The role emphasizes hands‑on development of LLM‑enabled systems, agent orchestration, data integration, and responsible AI practices, with opportunities to shape architecture, tooling and governance as the project scales.

Qualifications

• 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 POC.
• 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.

Responsibilities

• Design end-to-end agentic AI architecture with orchestration and tool integration.
• Develop AI agents to identify emerging fraud risks from approved sources.
• Build detection features and translate risks into analytics.
• Integrate agent workflows with data platforms (Redshift, Glue, PySpark).
• Develop modular, tested services and APIs with clear interfaces.
• Create CI/CD workflows for code, prompts and AI components with gates.
• Implement evaluation frameworks, governance and human-in-the-loop controls.
• Establish observability with dashboards and alerts for performance and costs.
• Collaborate with DSAD data scientists and analysts on domain needs.
• Produce technical documentation and runbooks; transfer knowledge to team.

Skills

Python

ML/AI apps

LangGraph

CrewAI

Data integration

Evaluation framework

Responsible AI

Analytical skills

Education

Degree in CS/AI/DS/SE

Job description

We're Hiring: MI/AI Engineer!

We are looking for a skilled MI/AI Engineer to join our team in Singapore. If you have a passion for machine intelligence and artificial intelligence, along with hands‑on experience in developing innovative solutions, this opportunity is perfect for you. Bring your expertise and help us build impactful technologies!

Location: Singapore, Singapore
Work Mode: Work from Office
Role: MI/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

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 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.