The posting
We are a fast-growing fintech company headquartered in Singapore, on a mission to drive financial inclusion through technology. Operating across multiple markets in Southeast Asia and beyond, we build and scale responsible credit and financial products for underserved individuals and small businesses.
Our AI Transformation team is building an internal platform that helps our people use AI effectively and develop software with greater speed and confidence. Initiatives include an internal agentic AI work platform for creating and running workflows across internal tools, AI-driven SDLC automation to help engineers plan, build, test, and ship software, and a centralised knowledge base that gives people and agents reliable access to company context.
This is a hands-on software engineering role; you’ll take ownership of meaningful parts of our AI platform, from design and implementation through deployment and operation. Your initial workstream will depend on the team’s priorities and your experience.
About the Role:
- Build production AI applications and services: Design backend services, APIs, integrations, and LLM-powered agent workflows that solve problems for internal users.
- Own delivery end to end: Clarify requirements, make pragmatic design choices, write maintainable code and tests, deploy safely, operate and troubleshoot production systems, and improve what you ship.
- Prototype and develop with AI: Use agentic AI coding tools to rapidly explore ideas, build prototypes, and accelerate development, while remaining accountable for design, security, and production quality.
- Measure and improve quality: Use tests, traces and observability, user feedback, and appropriate LLM or agent evaluations to diagnose failures and improve application behaviour.
- Make capabilities reusable: Work with internal teams to understand what works in practice, then turn successful patterns into documented platform components others can adopt.
- Build safely with partners: Collaborate with engineers, product, security, data scientists, and business teams to understand needs and improve AI platform adoption while considering security, data protection, and change management.
About You:
Must have
- Production backend engineering with Python: 3+ years of professional software engineering experience, including hands-on Python development building backend services or APIs. Experience designing, deploying, and operating backend services or platform capabilities in production.
- Databases: 2+ years of hands-on experience with a relational database such as PostgreSQL or MySQL, including schema design, SQL querying, indexing, and diagnosing query-performance issues.
- Cloud delivery and operations: 2+ years of hands-on experience deploying, operating and monitoring backend services on AWS or GCP, using Docker or Kubernetes and CI/CD pipelines.
- LLM and agentic application development: Hands-on experience building LLM-powered applications with agents that use tools or execute multi-step workflows. Relevant experience might include RAG, prompt management, evaluations, guardrails, multi-provider integrations, response streaming, or agent/LLM frameworks like LangGraph/LangChain and Strands.
- Agentic software engineering: Experience using AI coding agents, such as Claude Code or Codex, to prototype or implement changes, write tests, investigate failures, review code, etc.
- Problem solving and cross-functional collaboration: Able to break down ambiguous problems, weigh trade-offs, and turn solutions into clean, maintainable code. You are a strong team player who communicates decisions clearly, gives and takes feedback constructively, and works well with engineers, researchers, product, and users across teams.
Good to have
- Computer science foundation: Bachelor’s degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience.
- Scaling LLM platforms: Experience scaling and operating an LLM-powered platform serving 1,000+ active users, including managing latency, concurrency, reliability, and cost as usage grows.
- Knowledge of frontier LLM capabilities and tooling: Current knowledge of emerging LLMs, agent frameworks, and integration patterns, with hands-on experience in MCP/A2A, agent state and memory, evaluation and regression testing, safe code execution, model/provider routing, low-latency streaming to user interfaces, or model adaptation and fine-tuning, etc.
- Enterprise knowledge and search for AI: Experience building knowledge-access or retrieval systems that connect AI applications to internal documents and data, with attention to search relevance, source attribution, and access permissions.
- Production model development and serving: Hands-on experience adapting LLMs for real-world use cases, including supervised fine-tuning (SFT), parameter-efficient methods such as LoRA, preference optimization and RL-based post-training (e.g., DPO, RLHF, GRPO), distillation, and continued pretraining. Experience deploying and serving models in production using techniques such as quantization and optimized inference engines (e.g., vLLM), with attention to evaluation, latency, reliability, and cost.
- Startup or early-stage product experience: Experience building and shipping software in a small, fast-moving team, taking initiative when requirements are unclear, incorporating feedback quickly, and balancing speed with production quality.
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