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NEW 株式会社ジーニー Agent Harness Engineer / English【JAPAN AI採用】

ブックマーク一覧

正社員 1000万円

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
/////
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

※求人情報の応募要件全てに該当しなくても、企業様に対して内々に打診したり相談することが可能な場合もございます。一つでも当てはまる方は前向きにご検討下さい。

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

※更なる詳細事項は、カウンセリング（面談）時にお伝えします。

想定年収

1200 万円～ 2000 万円

勤務地

東京都新宿区西新宿6-8-1 住友不動産新宿オークタワー5/6階

勤務時間

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

休日・休暇

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

試用期間

1か月

加入保険

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）

受動喫煙対策の有無

有
敷地内禁煙（屋外に喫煙場所設置）

企業データ

会社名	株式会社ジーニー
事業内容	広告プラットフォーム事業/マーケティングSaaS事業/海外事業/デジタルPR事業
株式	◆東証グロース上場
設立	2010年4月14日
資本金	100百万円
従業員数	877名（連結）
決算月	3月
平均年齢	30.9歳 ※2021年7月末日時点

取材班による独自解説

広告プラットフォーム事業を中心に、企業のデジタルマーケティングを支援するSaaS事業を展開。テクノロジー企業を標榜し、生成AIを使ったサービスを手掛けるJAPAN AI株式会社を2023年に立ち上げたほか、北米の大手広告テクノロジー企業Zeltoを子会社化するなど事業拡大を図っている。
創業6年で国内トップクラス規模に拡大したアドプラットフォームを有し、DSPやDMP、マーケティングオートメーション領域についても、順調にシェアを伸ばしている。DSPは広告500社、SSPはメディア20000社ほどあり、業界No.1の地位を固くしている。
Web広告などで培ったアドテクノロジーのノウハウを活かし、DOOH（Digital Out of Home）という“屋外広告 × デジタル × データ活用”の世界に参入。これにより、ただの看板売りではなく、テック × データ × 広告のクロス領域での強みを持っている。

蓄積してきたデータを活かしたマーケティングSaaS事業も好調で、CRMの領域でシェアを伸ばしてきている。今後は海外展開を含め、さらに伸ばしていく方針。
エンジニアを内製化しているため、技術力の高さが売り。

Recruiting No.

01008655000631