JAPAN AI - Software Engineer, AI Platform

About JAPAN AI

JAPAN AI, Inc. was established in April 2023 as a group company of Geniee, Inc. (TSE Growth Market) with the mission of dramatically expanding human potential through AI technology. We drive cutting-edge AI R&D both domestically and internationally.

Our ambition goes far beyond building AI chatbots. We are building "the brain of the enterprise" — a next-generation core system where AI autonomously executes business operations by integrating all of a company's SaaS tools. With JAPAN AI STUDIO at the center, we are implementing a world where — given a database — no separate application is needed; AI performs the work and returns only the results.

Through the transformative power of AI, we aim to create new value and contribute to the advancement of society as a whole. Join us in leading AI innovation and shaping a future where technology empowers people to achieve more.

Why We're Hiring

"The brain of the enterprise" must never go down.

In a world where JAPAN AI STUDIO autonomously executes tasks such as approval workflows, resource allocation, and prospect discovery 24/7 for approximately 200 client companies, a platform uptime of 99.9% is the bare minimum. At the same time, optimizing inference and infrastructure costs in an environment where hundreds of workflows run concurrently — and improving developer experience — are critical demands.

If the Agent Harness Engineer is "the person who builds the engine," the Software Engineer (AI Platform) is "the person who builds the environment where the engine runs reliably." Kubernetes cluster design and operations, observability infrastructure, inference cost optimization, CI/CD pipeline development — this is a position that supports the entire infrastructure of "the brain of the enterprise" through the power of backend engineering.

Mission

"Support a world where 'the brain of the enterprise' never stops — 24/7, 365 days a year."

Design, build, and operate the shared foundation — backend services, execution environments, observability, and governance — that enables AI agents to operate safely, quickly, and reliably. Maximize the reliability and cost efficiency of the entire platform.

Role & Expectations

As a Software Engineer (AI Platform), you will power the reliability, performance, and cost efficiency of the entire AI platform through backend engineering.

Design, implement, and operate backend services while also optimizing Kubernetes clusters and cloud infrastructure

Design and build observability infrastructure (tracing, logging, metrics) to rapidly detect and resolve failures unique to AI agents

Deliver improvements with direct business impact through inference cost and infrastructure cost optimization

Maintain 99.9% uptime through SLI/SLO design and operations, on-call, and incident response

Improve developer experience for in-house engineers through CI/CD pipeline construction and development environment improvements

Why You'll Love This Role

At the intersection of Backend × Infrastructure — A new domain where you support the entire platform through the power of backend engineering.

Platform engineering for the AI era — Go beyond traditional infrastructure / SRE to tackle AI-specific challenges: inference cost optimization, GPU management, agent tracing, and more.

Large-scale cloud infrastructure design — Gain experience designing and operating large-scale distributed systems with Kubernetes, event-driven architectures, and autoscaling.

Cost optimization with real impact — Inference and infrastructure cost optimization directly translates to business impact. Improving $/request ripples across all products.

Powering every product — Support 99.9% uptime for a production environment used by ~200 companies. Every AI agent runs on the infrastructure you build.

Rapid-growth environment — In a startup that has grown to 200+ people and 9 products in just 3 years, you will have significant autonomy in technical decision-making.

Job Description

Backend Services & Platform Development

Design, implement, and operate backend services for the AI platform

Design, build, and operate Kubernetes clusters

Architect and optimize cloud infrastructure (GCP)

Codify and automate infrastructure with IaC (Terraform)

Cost/performance optimization (autoscaling, caching, batch processing, GPU management)

Observability & Governance

Design and build the observability stack (tracing, logging, metrics)

Implement AI agent-specific tracing (inference request tracking, tool call visualization)

Build data access and permission management infrastructure

Address security requirements

SRE & Reliability

Maintain platform uptime of ≥99.9%

Design and operate SLIs / SLOs

On-call, incident response, and post-mortems

Continuously improve incident MTTR

Developer Experience

Build and improve CI/CD pipelines

Maintain development and staging environments

Create and maintain infrastructure documentation for internal engineers

Example Scenarios

Scenario 1: Backend service optimization for the inference pipeline

A surge in inference requests degrades backend service latency. You analyze request patterns, redesign the caching strategy, and implement asynchronous processing in the backend service. Result: 40% improvement in P95 latency while reducing inference costs by 20%.

Scenario 2: Building the agent tracing infrastructure

Root-cause analysis for AI agent failures is taking too long. You design and implement an OpenTelemetry-based tracing infrastructure that visualizes the full flow from inference request → tool call → external API integration. Result: 50% reduction in MTTR.

Scenario 3: Cost optimization in a multi-tenant environment

In a multi-tenant environment serving ~200 concurrent customers, you build a dashboard that visualizes per-tenant resource consumption. By optimizing resource allocation based on usage patterns, you achieve a 15% improvement in infrastructure cost ($/request).

Key Results (KR/Metrics)

Platform uptime ≥ 99.9%

Agent execution latency P95 / P99

Infrastructure cost efficiency ($/request)

Developer experience score (internal NPS)

Incident MTTR ≤ target value

Team Structure

Approximately 120 members are part of the development organization.

Software Engineers (AI Platform) work across the following groups:

Infra — Cloud infrastructure and SRE

Data — Data pipelines and analytics infrastructure

Agent Harness — Agent execution framework

Closely collaborating roles:

Agent Harness Engineer — Agent execution infrastructure design and implementation

Agentic Product Engineer — Agent feature development

AI Quality Scientist — Evaluation pipeline collaboration

Product Manager — Product design and non-functional requirements definition

You May Be a Good Fit If You

Bachelor's degree or equivalent practical experience in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Mathematics, Physics, or related fields

3+ years of practical experience as a backend engineer

Production product development experience in Python

Design and operations experience on cloud platforms (AWS / GCP / Azure)

Understanding and operational experience with Kubernetes / container orchestration

Distributed system design and operations experience

Language requirement (at least one):

Japanese: Fluent — able to discuss product development without friction

English: Business level

Strong Candidates May Also Have

IaC practical experience (Terraform / Pulumi, etc.)

GPU cluster operations and optimization experience

ML infrastructure / MLOps construction experience

AI workload operations experience (inference servers, model serving)

Event-driven architecture experience (Kafka / RabbitMQ, etc.)

SRE / DevOps practices (SLI / SLO design, Chaos Engineering, etc.)

Security engineering experience

Technical communication ability in English

Tech Stack

Languages: Python (backend), TypeScript / React / Next.js (frontend) / NX

Infrastructure: GCP (containers / K8s), Docker, Terraform

Messaging: Kafka / Pub/Sub

Monitoring: Prometheus, Grafana, OpenTelemetry

CI/CD: GitHub Actions

Tools: Slack, Confluence, Linear, Google Workspace, GitHub, Notion

AI Dev Support: Claude Code MAX Plan, Cursor, ChatGPT, Devin

Hardware: Mac (Apple Silicon), dual monitors

Learning & Development Support

AI Tool Usage Support: Company covers the cost of using AI tools such as JAPAN AI SaaS services, Cursor, ChatGPT, Claude, etc.

Development Tool Support: If a desired development tool is paid, the cost is covered (up to ¥30,000 per year)

Book Purchase Assistance: Company covers the cost of purchasing books for learning, such as technical books (up to ¥30,000 per half-year)

Language Learning / Qualification Support: Company covers the cost of Japanese or English learning programs and qualification acquisition

Refresh Allowance: Company covers the cost of services used for personal refreshment (up to ¥5,000 per month)

Housing Allowance: Housing allowance provided for those living in designated areas (up to ¥30,000 per month)

Hiring Process

Application Review

Coding Assessment

Interviews (4–5 rounds)

Offer

A reference check will be conducted prior to the final interview.