仕事内容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.
Why We're Hiring
2025 was "the year of AI agents." 2026 is "the year of Agent Harness."
In a world where JAPAN AI STUDIO autonomously executes hundreds of workflows as "the brain of the enterprise," agent performance is not determined by the model alone. The Agent Harnessーthe control layer that wraps the model and manages session state, checkpoints, guardrails, context injection, and tool executionーis the key that transforms an agent from "works in a demo" to "trusted in production."
"The brain of the enterprise" approves requests, allocates resources, and discovers prospectsーthe Agent Harness is the heart that controls each of these actions safely, quickly, and reliably.
JAPAN AI is hiring Agent Harness Engineers to design and implement this Agent Harness in-house and build it as the shared foundation across all products.
Mission
"Design the heart of 'the brain of the enterprise.'"
Design and implement the Agent Harnessーexecution engine, orchestration, guardrails, memory, and model routingーthat enables AI agents to operate safely, quickly, and reliably. Build the control foundation for hundreds of workflows running on JAPAN AI STUDIO, entirely in-house.
What Is an Agent Harness?
An Agent Harness is the control and execution infrastructure layer that wraps AI models. While Agent Frameworks (e.g., LangChain) handle agent construction , the Agent Harness handles agent control and operation .
Backend Engineer
What you build : Web APIs, microservices
Relationship with AI/ML : Calls ML models via API
State management : Stateless request/response
Safety controls : Authentication, authorization, input validation
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Agent Harness Engineer
What you build : LLM-centric agent execution engines, SDKs, orchestrators
Relationship with AI/ML : Designs model routing, RAG integration, context injection, and inference optimization at the system level
State management : Agent session management, checkpoints, long-term memory, working memory
Safety controls : Guardrail/policy execution engineーa rule execution layer that controls LLM output
Role&Expectations
As an Agent Harness Engineer, you will design and implement the agent control and execution infrastructure, leveraging your AI/ML knowledge.
Design and implement the execution engine (Graph Runtime / State Machine) with deep understanding of LLM / AI agent operating principles
Own AI-specific system design including model routing, context management, and memory infrastructure (long-term memory, working memory)
Design and develop the Agent SDK used by 120 in-house engineers
Build the guardrail / policy execution engine to safely control agent behavior
Collaborate with Research Engineers to integrate the latest research outcomes into the production infrastructure
Why You'll Love This Role
Build the Agent Harness in-houseーDesign and implement the hottest architectural concept of 2026 without relying on OSS. Stand at the industry's cutting edge.
At the intersection of AI/ML×BackendーDesign and implement the agent execution infrastructure with deep understanding of LLM operating principles. Neither pure infrastructure nor pure MLーa new domain.
Foundation software designerーThis is not a job writing YAML. You will build SDKs, execution engines, and orchestrators in code. Low-level knowledge directly applies.
Developer experience architectーDesign the SDK and toolchain used by 120 in-house engineers, improving productivity across the entire development organization.
Powering every productーIn a production environment used by～200 companies, every AI agent runs on the Harness you build.
Rapid-growth environmentー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
Agent Harness design&implementation
Design and implement the agent execution engine (Graph Runtime / State Machine)
Design and develop the Agent SDKーthe interface for in-house engineers to build agents
Implement session management, checkpoint, and recovery mechanisms
Build the guardrail / policy execution engineーa rule execution infrastructure that controls agent behavior
AI/ML System Integration
Model routingーoptimal routing of inference requests across multiple LLM providers and model types
Design context management and memory infrastructure (long-term memory, working memory, RAG integration)
Optimize inference pipelines (latency reduction, cost efficiency, caching strategies)
Integrate latest research findings into the production infrastructure in collaboration with Research Engineers
Orchestration&performance
Develop workflow orchestration and queuing systems
Cost/performance optimization (autoscaling, caching, batch processing)
Inference request routing and load balancing
Reliability&Operations
Maintain platform uptime of≧99.9%
Incident response and post-mortems
Design data access and permission management infrastructure
Key Results (KRs / Metrics)
Agent SDK adoption rate (in-house team usage rate and satisfaction)
Agent execution success rate (task completion rate, checkpoint recovery success rate)
Harness-attributed failure rate (guardrail breach rate, state inconsistency rate)
Execution latency P95 / P99 (Harness layer overhead)
Inference cost efficiency (cost optimization through model routing)
Developer experience score (internal NPS for SDK / API)
Team Structure
Approximately 120 members are part of the development organization.
Agent Harness Engineers work across the following groups:
InfraーCloud infrastructure and SRE
DataーData pipelines and analytics infrastructure
Agent HarnessーAgent execution framework
Closely collaborating roles:
Agentic Product EngineerーAgent feature development (SDK users)
Research EngineerーR&D and integration of new methods into the infrastructure
AI Quality ScientistーEvaluation pipeline collaboration
Product ManagerーProduct design and non-functional requirements definition
経験・資格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
Practical experience as a backend engineer
Production product development experience in Python
Experience designing and implementing production systems that leverage LLM / AI agents
Experience designing and implementing distributed systems (including design and coding, not just operations)
Experience designing and implementing RESTful APIs / gRPC
Language requirement (at least one of the following):
Japanese: Fluentーable to discuss product development without friction
English: Business level
Strong Candidates May Also Have
Agent Framework / Agent Harness design and implementation experience (LangChain / LangGraph / AutoGen, etc.)
Production operations experience on cloud platforms (AWS / GCP / Azure)
Understanding of RAG systems, vector databases, and memory architectures
Model routing and inference optimization experience
Foundation software development experience in Go (SDKs, runtimes, frameworks, etc.)
Deep understanding of Kubernetes / container orchestration
Event-driven architecture experience (Kafka / RabbitMQ, etc.)
Experience implementing safety guardrails, policy execution, and AI observability
ML infrastructure / MLOps construction experience
Technical communication ability in English
Tech Stack
Languages : Python, Go (backend / infrastructure), TypeScript / React / Next.js (frontend), NX
Infrastructure : GCP (containers / K8s), Docker, Terraform
Messaging : Kafka, Pub/Sub
Monitoring : Prometheus, Grafana, OpenTelemetry
Tools : Slack, Confluence, Linear, Google Workspace, GitHub, Notion
AI Dev Support: Claude Code MAX Plan, Cursor, ChatGPT, Devin
Workstation : Mac (Apple Silicon), dual monitor setup
勤務地
想定年収
福利厚生・待遇
勤務条件10:00～19:00
※土日祝は休業日となります
※出向の場合は、出向先の規程に準じます
Work Style
Hybrid work : 3 days in office, 2 days remote
Flexible working hours : Core time is negotiable
Flexibility : Future consideration for more flexible work styles is possible
完全週休二日制
所定休日：土・日・祝日
休暇：年次有給休暇、夏季休暇（3日）、年末年始休暇（12月31日～1月3日）、慶弔休暇
1か月
社会保険完備（健康保険：関東ITソフトウェア健康保険組合）
Recruiting No.
社名
事業内容
株式
設立
資本金
従業員数
決算月
平均年齢
【東証プライム上場 日本最大級の発電会社】 電力需給統括部 電力需給システム開発プロジェクトリード担当
【東証プライム上場 有名電機メーカーグループ】 技術系総合職
【東証プライム上場 日本を代表する総合重工業メーカー】 社内SE（クラウドサービス基盤の構築・運用を担うインフラエンジニア）
全員参加型のビジネス変革が成果を生み出し、キャリア人材の成長機会が増え続けています。
人々の生活や命を支えるため、「食料・水・環境」分野で地域に根ざした事業にチャレンジする
高度な専門性を持ち、お客様の業務に精通したSEと営業が一丸となり、 お客様のビジネスの成長を “攻めと守り”のITで支援。
世界に向かうデジタルビジネスのパートナーとして、売上拡大とコスト最適化を支援しています。
エネルギー、インフラ、ストレージ。3つの注力事業において、新しい人材が 「新生東芝」 を動かし始めています。
グローバル展開する企業のプライムパートナーとして、経営から製造現場まで、多様な課題の解決をITで支援。
