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.
