AI Application Engineer (FinTech Payments, AI Agents, RAG & Workflow Automation)

My Client:

My client is a MAS-licensed Major Payment Institution (MPI) and Digital Payment Token (DPT) service provider in Singapore. The company operates across multiple international markets and provides payment infrastructure. As part of its AI transformation initiative, the company is building intelligent systems across risk management, compliance, merchant operations, and settlement workflows.

The Role:

The team is looking for an experienced AI Application Engineer to drive AI adoption from proof-of-concept to production-scale deployment within a regulated fintech environment. You will work at the intersection of AI, backend engineering, and fintech operations, designing and building AI-powered applications that automate complex business processes.

Job Responsibilities:

• Build AI-powered applications using LLM APIs, including intelligent assistants, workflow automation tools, reporting systems, and agent-based solutions.
• Design and optimize RAG architectures, including chunking strategies, embedding pipelines, vector databases, and retrieval quality improvements.
• Develop AI Agents with function calling, tool usage, memory management, and multi-step workflow orchestration capabilities.
• Apply prompt engineering and evaluation methodologies to improve model accuracy, reliability, and business alignment.
• Design reusable AI services, APIs, and microservices following scalable software engineering principles.
• Integrate AI solutions with payment, risk control, compliance, merchant operations, and settlement systems.
• Collaborate closely with product, operations, and business teams to identify and deliver high-impact AI use cases.

Job Requirements:

• 5+ years of software engineering experience with at least 2 years building and deploying AI applications in production environments.
• Strong programming skills in Python or Java. Solid experience with backend development, microservices architecture, MySQL, Redis, and messaging systems.
• Hands-on experience with LLM applications, Prompt Engineering, RAG systems, AI Agents, Function Calling, and Tool Use frameworks.
• Strong system design and architecture skills, with the ability to build scalable, modular, and service-oriented AI platforms.
• Experience integrating AI solutions into real-world business workflows and operational systems.

Nice to Have:

• Experience in fintech, payments, digital assets, risk management, compliance, AML, KYC, or fraud detection.
• Experience with LangChain, LlamaIndex, Spring AI, LangGraph, AutoGen, CrewAI, or similar frameworks.

Why Join:

• Build production-grade AI systems in a real-world fintech environment.
• Work on high-impact use cases across payments, compliance, risk control, and operational automation.
• Opportunity to drive AI adoption at company scale rather than isolated experimentation.