Applied AI Engineer

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Job Description

Applied AI Engineer

AI-Native Financial Technology Platform

Location: Singapore (Remote)

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The Company

Our client is a rapidly growing financial technology company transforming the way consumers manage, protect and grow their finances through AI-native products. Operating across Southeast Asia, the company develops intelligent financial solutions spanning insurance, digital banking, payments, investing and personal finance.

With a highly international engineering team and a strong product-driven culture, the organization is committed to building scalable AI systems that automate real-world workflows and deliver exceptional customer experiences.

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The Position

The Applied AI Engineer will be responsible for designing, developing and deploying production-grade AI solutions that power next-generation financial products and intelligent operational workflows.

Working closely with Product, Engineering and Business teams, the successful candidate will build AI agents, LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, intelligent automation platforms and AI-assisted user experiences across customer onboarding, KYC, customer support, fraud detection, risk management, CRM, document processing and internal operations.

The role requires balancing innovation with practical engineering considerations including reliability, latency, scalability, security, compliance, cost optimization and user trust. The successful candidate will continuously evaluate, improve and optimize AI systems in production while transforming manual business processes into intelligent, scalable and highly reliable AI-enabled solutions.

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Key Responsibilities

• Design, develop and deploy AI-native applications, AI agents and intelligent workflow automation solutions.
• Build production-grade applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), tool calling and prompt engineering techniques.
• Develop AI-powered capabilities across customer onboarding, KYC, AML, fraud detection, customer support, CRM, document intelligence and operational workflows.
• Collaborate with Product Managers, Engineers and Business stakeholders to identify high-value AI opportunities and deliver scalable solutions.
• Design robust AI systems with strong emphasis on accuracy, evaluation, observability, latency, scalability, reliability and cost efficiency.
• Develop backend services, APIs and microservices supporting AI applications.
• Build and integrate enterprise AI solutions with internal systems, databases and third-party platforms.
• Evaluate AI model performance and continuously improve quality through testing, prompt optimization, guardrails and production monitoring.
• Implement secure, compliant and scalable AI solutions suitable for regulated financial environments.
• Contribute to engineering best practices, software architecture, CI/CD pipelines, testing frameworks and production deployments.
• Stay current with emerging AI technologies, frameworks and engineering best practices.

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The Person

The successful candidate will possess a Degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning or a related discipline, together with 3–8+ years of hands-on software engineering experience developing scalable production systems. Candidates should demonstrate a strong track record of building and deploying AI-powered applications rather than purely research-focused projects.

Technical Skills & Experience

• Strong proficiency in Python, with experience in TypeScript, JavaScript, Go or similar modern programming languages.
• Hands-on experience building applications using Large Language Models (LLMs) including OpenAI, Anthropic, Gemini, Llama or equivalent foundation models.
• Practical experience developing AI Agents, Agentic Workflows, Retrieval-Augmented Generation (RAG), tool calling, function calling, prompt engineering, and multi-agent systems.
• Experience implementing vector databases such as Pinecone, Weaviate, Milvus, ChromaDB or pgvector.
• Experience using AI orchestration frameworks including LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, DSPy or similar technologies.
• Strong understanding of LLM evaluation, hallucination mitigation, guardrails, prompt optimization, AI observability, and production reliability.
• Experience designing and developing RESTful APIs, microservices, asynchronous services and scalable backend architectures.
• Experience with SQL, NoSQL databases, caching technologies and message queues.
• Familiarity with AWS, Microsoft Azure or Google Cloud Platform, including containerization using Docker and orchestration with Kubernetes.
• Experience implementing CI/CD pipelines, automated testing, monitoring, logging and production deployment practices.
• Good understanding of distributed systems, scalability, latency optimization and cost-efficient AI inference.
• Experience integrating AI applications with enterprise platforms, external APIs and business systems.
• Strong knowledge of software engineering principles including system design, design patterns, code quality, testing and version control.

Preferred Experience

• Experience within FinTech, Digital Banking, Payments, Insurance, Wealth Management or other regulated financial services environments.
• Exposure to KYC, AML, Fraud Detection, Credit Risk, Customer Support Automation, CRM or operational workflow automation.
• Experience developing AI copilots, autonomous agents, document intelligence solutions or enterprise knowledge assistants.
• Familiarity with MLOps practices, model deployment pipelines and AI monitoring frameworks.
• Understanding of data privacy, cybersecurity and AI governance within regulated industries.

Personal Attributes

• Hands-on builder with a passion for solving real-world business problems using AI.
• Strong analytical and problem-solving capabilities with a pragmatic engineering mindset.
• Demonstrated ability to rapidly prototype, build and deploy production-ready AI systems.
• Excellent communication and stakeholder management skills, with the ability to work effectively across Engineering, Product and Business teams.
• Self-motivated, adaptable and comfortable working in a fast-paced, high-growth technology environment.
• Proven ability to take ownership, deliver measurable business impact and continuously improve AI products based on production feedback and user outcomes.