Money Forward Vietnam

Platform and engineering depth (from ERP / platform practice)

• 7+ years building and operating web applications in production, with a platform bias: APIs, microservices, distributed systems, and reliability under load.
• Platforms and internal capabilities: experience building or operating platforms, internal tools, SDKs, or shared infrastructure consumed by other teams.
• Cloud and operations: AWS (preferred), Docker, Kubernetes, Infrastructure as Code (e.g. Terraform, K8s manifests, or equivalent).
• API design: REST and streaming where needed; developer experience, versioning, and reliability as first-class concerns.
• LLM integration in production: experience wiring LLM APIs (e.g. OpenAI, Gemini, Anthropic) into real systems, with attention to failure modes, cost, and safety.
• End-to-end ownership: design through rollout; clear decision-making and stakeholder communication; mentoring and technical design leadership.
• AI-assisted development in daily workflow; LLMOps and automation for model-related pipelines where applicable.

Required experience and skills (must have)

• Generative AI: Enthusiastic, daily use of generative AI and advanced AI tooling to streamline work and materially accelerate delivery—while remaining accountable for quality and fit-for-purpose output in production-leaning systems.
• Product mindset: Proven track record translating high-level product requirements into detailed requirements and comprehensive technical requirements through close partnership with Product Managers and domain stakeholders.
• Communication: Strong verbal and written English for clarity and alignment in distributed, multinational product engineering teams.
• Large-scale B2B SaaS (especially back-office), backend, and cloud—design and operations, not only greenfield coding.
• Complex domain models, database design, and distributed-system thinking (consistency, idempotency, backpressure, failure domains).
• Elite code review and refactoring to raise maintainability and robustness of existing and AI-generated code.
• Willingness to lead the shift toward reviewing and steering AI output rather than only writing every line from scratch.

Preferred (nice to have)

• SRE practices; observability (e.g. Datadog, logging, tracing).
• Multi-tenant SaaS or developer platforms.
• Enterprise security depth: authN/authZ, encryption, threat models for SaaS.
• Generative AI in production: prompt injection and other AI-specific controls; AI agent patterns (tool use, function calling, memory, multi-turn guardrails).
• Cross-team work with product and security; CI/CD and infrastructure automation at org scale.
• Japanese language skills (not required) — a strong plus for collaboration with Japan-based teams, product, and stakeholders.