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AI EngineerThunes · Singapore, Central, Singapore
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Thunes

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AI Engineer

🗓️ Posted 2026-08-31 Singapore, Central, Singapore Hybrid ICT
💰 Salary Not shared 📊 Market S$6,000–S$10,000/mo

About the Company

Thunes is the Smart Superhighway for money movement around the world. Thunes' proprietary Direct Global Network allows Members to make payments in real-time in over 130 countries and more than 80 currencies. The Network connects directly to over 7 billion mobile wallets and bank accounts worldwide, via more than 350 different payment methods, such as GCash, M-Pesa, Airtel, MTN, Orange, JazzCash, Easypaisa, AliPay, WeChat Pay and many more. Members of Thunes' Direct Global Network include gig economy giants like Uber and Deliveroo, super-apps like Grab and WeChat, MTOs, fintechs, PSPs and banks. Thunes' Direct Global Network differentiates itself through its worldwide reach, in-house Smart Treasury Management Platform and Fortress Compliance Infrastructure, ensuring Members of the Network receive unrivalled speed, control, visibility, protection and cost efficiencies when making real-time payments globally. Headquartered in Singapore, Thunes has offices in 12 locations, including Barcelona, Beijing, Dubai, London, Manila, Nairobi, Paris, Riyadh, San Francisco, Sao Paulo and Shanghai.

About the Job

We are looking for a highly driven, self-motivated, and technically excellent engineer who is truly excited about building the "AI Operating System" of a high-growth Fintech. You will need to combine a startup mindset with the rigor of a regulated financial institution, moving beyond simple chatbots to build "Stateful" Agentic Systems and Multimodal RAG pipelines that can reason across text, charts, and financial tables. This role is responsible for developing core generative capabilities and getting them to production in the most efficient, secure, and observable way possible. Solutions architect bridges our AI tech stack with our enterprise data lake on AWS. This role relies on "Deployment Autonomy", meaning you architect, implement, test, and deploy your own solutions.

Role & Responsibilities

  • ● Architect production-grade RAG workflows on cloud resources (e.g. AWS, GCP) using reliable tools (e.g. LangGraph / LlamaIndex) and build agents that can plan multi-step financial tasks
  • ● Implement multimodal reasoning pipelines that ingest and reason over mixed-media financial documents (e.g. Charts, Graphs, Tables)
  • ● Act as the internal adversary (Red Teaming) by designing cases to stress-test models against Prompt Injection, Jailbreaking and PII Leakage before they touch sensitive data
  • ● Define and implement the "Definition of Done" for AI using quantitative evaluation with evaluation tools (e.g. DeepEval) to score models on Faithfulness and Relevancy and validate model upgrades and cost-optimisation experiments
  • ● Champion Deployment Autonomy by containerising solutions (e.g. Docker), defining the infrastructure and deploying to cloud resources via GitLab CI
  • ● Ensure governance by implementing deterministic guardrails (e.g. NeMo Guardrails, Guardrails AI) to enforce JSON output schemas and block non-compliant financial advice

Requirements

Education

  • ● Multi-Cloud Fluency: Expertise in architecting solutions on major cloud platforms (e.g. AWS, GCP) and Mastery of cloud native LLM ecosystems (e.g. Bedrock, Vertex AI) with Strong operational grasp of cloud services (e.g. IAM, Storage)
  • ● Mastery of GenAI Orchestration: Expert level skills in LangChain or LangGraph (specifically for stateful, multi-turn agents) or LlamaIndex and other equivalent tools/libraries
  • ● Bachelor's degree in a STEM field (Computer Science, Engineering, Mathematics, Physics)

Certifications

  • ● Google Professional Machine Learning Engineer certification (preferred)
  • ● AWS Certified Machine Learning - Specialty certification (preferred)

Experience

  • ● 5+ years of total AI engineering experience, with 2+ years of hands-on experience building and deploying NLP or GenAI solutions in production
  • ● Experience in Evaluation & Monitoring: Proficiency with open source tools for testing (e.g. DeepEval) and Experience implementing full tracing and observability tools (e.g. LangSmith, Langfuse, Arize Phoenix)
  • ● Retrieval Engineering: Hands-on experience managing vector databases (e.g. Vertex AI Vector Search, Weaviate, Milvus or pgvector) and Understanding of indexing and chunking strategies for hybrid search (Dense + Sparse)
  • ● Experience in Security & Red Teaming: Knowledge of adversarial testing frameworks (e.g. Giskard, PyRIT) to scan for LLM vulnerabilities

Skills

  • ● Passionate about MLOps: Ability to write GitLab CI pipelines (automated testing, linting, scanning) and strong Docker skills (multi-stage builds)
  • ● Python Expertise: Proficiency with data validation (e.g. Pydantic) and asynchronous REST APIs (e.g. FastAPI)

Preferred (nice to have)

  • ● Prior experience in Payments, Banking or Finance Services