Staff AI Engineer (AI Agents Development), CDAO Office, Tokyo

**About the company:** Money Forward Minato-ku, Tokyo Money Forward is a fintech startup delivering tools to visualize and improve both individuals'​ and companies'​ financial health. **Responsibilities:** Agentic System Design: Architect, build, and scale multi-agent systems and LLM-powered services for customer-facing products — from prototype to production, built to sustain heavy real-world load. Agent Engineering: Design reliable tool-use, function-calling, memory, multi-turn, protocols and modern orchestration frameworks; establish guardrails, evaluation, and safe fallback behavior. Backend & Infrastructure at Scale: Own backend services, APIs, and the infrastructure that agents run on — high availability, low latency, secure secrets/credential handling, and horizontal scalability under production traffic. Evaluation & Benchmarking (core focus): Own how we measure agent quality. Build the evaluation harness end to end — representative task sets, fixed inputs and reference outcomes, repeatable runners, and scoring across task completion, correctness, latency, cost, and human-intervention rate. Establish LLM-as-a-judge and offline/online eval, wire evaluation into CI as a regression gate, and grow from a pilot task set to a durable benchmark suite that product and QA can trust as an acceptance gate. Durable & Long-Running Agent Execution: Design the runtime for agentic tasks that run for ten minutes or longer — durable task lifecycle (submit, status, timeout, cancel, complete, fail), isolated sandboxes for code and file execution, artifact generation and retrieval, and a harness for retries, back-off, checkpointing, resume, and self-recovery. Reliability & Cost: Identify bottlenecks; optimize latency, throughput, token/compute cost, and reliability; instrument observability across the agent lifecycle. Safety & Governance: Apply security and governance best practices (input validation, content filtering, PII handling, HITL escalation, risk-based sampling) appropriate for customer-facing systems. Cross-Functional Technical Leadership & Mentorship: Partner day-to-day with product, business, backend, frontend, infrastructure/SRE, and QA to translate business goals into agent architecture; align standards across BE/AI/Infra, drive system-level decisions that span team boundaries, and mentor engineers on agent development, LLM integration, and evaluation methodology. Requirements 7+ years of professional software engineering experience, with strong recent hands-on delivery (not purely managerial). Deep backend engineering expertise — designing, building, and operating large-throughput , low-latency production systems and secure APIs. Strong infrastructure skills: cloud ( AWS and/or Azure ), containers ( Docker ), orchestration ( Kubernetes ), Infrastructure as Code ( Terraform ), and CI/CD — including operating and troubleshooting production services. Proven experience building AI agents / agentic orchestration for real products, with hands-on use of modern agent frameworks — especially the Claude Agent SDK (agentic loop, tool use, sessions, sandboxed execution, Skills). Comparable depth in LangGraph or similar orchestration frameworks is relevant, as is judgment on when a single agentic loop beats a multi-stage pipeline. Strong command of Python or TypeScript for building production services (e.g., FastAPI / FastMCP , async request handling, dependency and lifecycle management, ASGI/Node runtimes) Solid understanding of MCP, REST API, GraphQL protocols for tool integration and agent-to-agent communication. Demonstrated experience building agent evaluation from scratch — not just consuming dashboards. You have designed task sets and scoring rubrics, run controlled baseline-versus-variant experiments, and used the results to drive architecture decisions. Experience with LLM tracing and observability (OpenTelemetry / OpenLLMetry, Langfuse, or equivalent) and with performance and load testing of production services. Experience building or operating durable / long-running execution infrastructure — job and task lifecycle management, isolated sandboxes (containers, microVMs, or managed sandbox services), artifact storage, and failure recovery. Strong grasp of core CS fundamentals: data structures, algorithms, software design, and engineering best practices. English: Business level (equivalent to TOEIC 700 or above) Nice to haves While not specifically required, tell us if you have any of the following. Experience shipping AI features in customer-facing / B2B SaaS products under real security and compliance constraints. Experience with guardrails, safety, and governance frameworks for LLM/agent systems (e.g., OWASP-style threat modeling for agents). Experience with cost optimization for LLM usage (context compression, model routing, non-frontier models, gateways). Fluent use of AI-assisted development tools (Claude Code, Cursor, GitHub Copilot, Codex) with sound judgment on when to delegate to AI and when to verify. Japanese: Not required but nice to have Compensation ¥11,004,000 ~ ¥20,004,000 annually. **Requirements:** 7+ years of professional software engineering experience, with strong recent hands-on delivery (not purely managerial). Deep backend engineering expertise — designing, building, and operating large-throughput , low-latency production systems and secure APIs. Strong infrastructure skills: cloud ( AWS and/or Azure ), containers ( Docker ), orchestration ( Kubernetes ), Infrastructure as Code ( Terraform ), and CI/CD — including operating and troubleshooting production services. Proven experience building AI agents / agentic orchestration for real products, with hands-on use of modern agent frameworks — especially the Claude Agent SDK (agentic loop, tool use, sessions, sandboxed execution, Skills). Comparable depth in LangGraph or similar orchestration frameworks is relevant, as is judgment on when a single agentic loop beats a multi-stage pipeline. Strong command of Python or TypeScript for building production services (e.g., FastAPI / FastMCP , async request handling, dependency and lifecycle management, ASGI/Node runtimes) Solid understanding of MCP, REST API, GraphQL protocols for tool integration and agent-to-agent communication. Demonstrated experience building agent evaluation from scratch — not just consuming dashboards. You have designed task sets and scoring rubrics, run controlled baseline-versus-variant experiments, and used the results to drive architecture decisions. Experience with LLM tracing and observability (OpenTelemetry / OpenLLMetry, Langfuse, or equivalent) and with performance and load testing of production services. Experience building or operating durable / long-running execution infrastructure — job and task lifecycle management, isolated sandboxes (containers, microVMs, or managed sandbox services), artifact storage, and failure recovery. Strong grasp of core CS fundamentals: data structures, algorithms, software design, and engineering best practices. English: Business level (equivalent to TOEIC 700 or above) **Nice to have:** While not specifically required, tell us if you have any of the following. Experience shipping AI features in customer-facing / B2B SaaS products under real security and compliance constraints. Experience with guardrails, safety, and governance frameworks for LLM/agent systems (e.g., OWASP-style threat modeling for agents). Experience with cost optimization for LLM usage (context compression, model routing, non-frontier models, gateways). Fluent use of AI-assisted development tools (Claude Code, Cursor, GitHub Copilot, Codex) with sound judgment on when to delegate to AI and when to verify. Japanese: Not required but nice to have **Compensation:** ¥11,004,000 ~ ¥20,004,000 annually.