Backend Engineer, Agent Development - AI Lab (AILAB)

Job Description:

Business Overview

Rakuten Group, Inc. is a global leader in internet services and has a diverse ecosystem spanning across e-commerce, fintech, communications and more serving approximately 1.8 billion members worldwide. Founded in Tokyo in 1997, the Group operates in over 30 countries and regions with more than 30,000 employees.

Department Overview

The AI & Data Division (AIDD) creates powerful, customer-focused search, recommendation, data science, advertising, marketing, price, and inventory optimization solutions for businesses across the Rakuten group. As part of AIDD, AI Lab builds new solutions that have the potential to transform how users interact with Rakuten services. Our goal is to drive innovation by developing new products and capabilities that deliver significant impact over longer timeframes using AI.

Position:

Position Details

We're building agents that work inside Rakuten's commerce ecosystem - shopping assistants that understand what a customer actually wants and find it across a vast, messy, multi-merchant catalog; and merchant-facing agents that handle listing, pricing, and operations work that used to require a human in a dashboard. The distinguishing constraint of this role: these agents take real actions on real commerce state. They add to carts, apply coupons, initiate returns, update listings, trigger fulfillment. A hallucinated sentence is an inconvenience; a hallucinated order is an incident. You'll build the backend that makes agent actions safe, auditable, and reversible - while keeping them fast enough for a shopper who won't wait four seconds.

<Main Responsibility>

・Build the backend services behind conversational and autonomous commerce agents: intent understanding, multi-turn task execution, tool orchestration, human confirmation checkpoints

・Own the commerce tool layer - catalog search, product detail, inventory and price lookup, cart, checkout, order status, returns - exposing internal commerce APIs to agents with strict contracts, auth scoping, and blast-radius limits

・Solve grounding on a live catalog: retrieval over tens of millions of SKUs, attribute and variant normalization, freshness guarantees on price and stock, and preventing agents from confidently recommending things that don't exist or can't ship

・Design transactional safety for agent actions: idempotency, confirmation and rollback flows, spend and scope limits, permission models, and full audit trails for every action an agent takes on a user's or merchant's behalf

・Build evaluation and observability for commerce outcomes - task success, recommendation relevance, cart and conversion impact, containment and escalation rates - with tracing across multi-step runs and regression gates before release

・Optimize latency and unit economics: retrieval and tool-call budgets, model routing, prompt caching, cost per resolved task measured against the revenue or support cost it moves

・Work directly with commerce product, search/recsys, merchant platform, and trust & safety teams to ship agents through launch review into production

Mandatory Qualifications:

・Over 5 years building and operating production backend services; strong in Python (Go or Java a plus)

・Experience developing ML-based production systems - search, recommendation, ads ranking, personalization, or fraud/risk. You've owned a system where model quality and system quality were the same problem

・Experience with high-traffic transactional or e-commerce backends: catalog, inventory, cart/checkout, payments, or order management - you understand why correctness and consistency matter more here than in most systems

・Track record of shipping quality improvements measured by data: offline evaluation, online A/B testing, and the judgment to know when they disagree and why

・Solid distributed systems fundamentals: async and event-driven architecture, queues, concurrency, idempotency, distributed tracing

・Hands-on experience shipping LLM-backed systems to production - not just API calls, but the infrastructure around them

・Direct exposure to agentic patterns: tool/function calling, orchestration frameworks, multi-turn state management

・Cloud and containerized deployment experience (Kubernetes, CI/CD, IaC)

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