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【JAPAN AI】Agent Harness Engineer / English

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雇用形態	正社員
給与	1200万円〜2000万円（年収 12,000,000 円 - 20,000,000円 Monthly: 857,143－1,428,571 (incl. 45h fixed overtime) Stock options available Reviews & bonuses: twice/year OT beyond 45h paid separately Negotiable based on experience and skills ）
勤務地	東京都
勤務地詳細	東京都新宿区西新宿住友不動産新宿オークタワー５/６階
職種	Rubyエンジニアデータサイエンティスト【JAPAN AI】Agent Harness Engineer / English 【JAPAN AI】Agent Harness Engineer / English ITエンジニア企画・マーケ
業種	インターネット関連 IT通信インターネット

必須条件

（業務経験1年以上）

業務内容

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.
Related URLs
Our Website
Company Introduction Materials
Tech Blog
Careers
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 the control layer that wraps the model and manages session state, checkpoints, guardrails, context injection, and tool execution 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 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 execution engine, orchestration, guardrails, memory, and model routing 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 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 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 Design and implement the agent execution infrastructure with deep understanding of LLM operating principles. Neither pure infrastructure nor pure ML a new domain.
Foundation software designer 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 Design the SDK and toolchain used by 120 in-house engineers, improving productivity across the entire development organization.
Powering every product In a production environment used by －200 companies, every AI agent runs on the Harness 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
Agent Harness design & implementation
Design and implement the agent execution engine (Graph Runtime / State Machine)
Design and develop the Agent SDK the interface for in-house engineers to build agents
Implement session management, checkpoint, and recovery mechanisms
Build the guardrail / policy execution engine a rule execution infrastructure that controls agent behavior
AI/ML System Integration
Model routing 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 Cloud infrastructure and SRE
Data Data pipelines and analytics infrastructure
Agent Harness Agent execution framework
Closely collaborating roles:
Agentic Product Engineer Agent feature development (SDK users)
Research Engineer R&D and integration of new methods into the infrastructure
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
5+ years of 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 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
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)
e.g., gym, yoga, chiropractic, aquarium, movies, theme park tickets, etc.
Housing Allowance
Housing allowance provided for those living in designated areas (up to 30,000 per month)

求人詳細

■職種 / 募集ポジション

【JAPAN AI】Agent Harness Engineer / English

■求人タイトル

【JAPAN AI】Agent Harness Engineer / English

■雇用形態
正社員

■給与
年収 12,000,000 円 - 20,000,000円
Monthly: 857,143－1,428,571 (incl. 45h fixed overtime)
Stock options available
Reviews & bonuses: twice/year
OT beyond 45h paid separately
Negotiable based on experience and skills

■勤務地
東京都新宿区西新宿住友不動産新宿オークタワー５/６階

■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

■Hiring Process
1. Application Review
2. Coding Assessment
3. Interviews (45 rounds)
4. Offer

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

■仕事についての詳細

＜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.
Related URLs
Our Website
Company Introduction Materials
Tech Blog
Careers

＜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 the control layer that wraps the model and manages session state, checkpoints, guardrails, context injection, and tool execution 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 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 execution engine, orchestration, guardrails, memory, and model routing 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 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 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 Design and implement the agent execution infrastructure with deep understanding of LLM operating principles. Neither pure infrastructure nor pure ML a new domain.
Foundation software designer 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 Design the SDK and toolchain used by 120 in-house engineers, improving productivity across the entire development organization.
Powering every product In a production environment used by －200 companies, every AI agent runs on the Harness 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＞
Agent Harness design & implementation
Design and implement the agent execution engine (Graph Runtime / State Machine)
Design and develop the Agent SDK the interface for in-house engineers to build agents
Implement session management, checkpoint, and recovery mechanisms
Build the guardrail / policy execution engine a rule execution infrastructure that controls agent behavior
AI/ML System Integration
Model routing 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 Cloud infrastructure and SRE
Data Data pipelines and analytics infrastructure
Agent Harness Agent execution framework
Closely collaborating roles:
Agentic Product Engineer Agent feature development (SDK users)
Research Engineer R&D and integration of new methods into the infrastructure
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
5+ years of 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 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

＜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)
e.g., gym, yoga, chiropractic, aquarium, movies, theme park tickets, etc.
Housing Allowance
Housing allowance provided for those living in designated areas (up to 30,000 per month)

■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

■Hiring Process
1. Application Review
2. Coding Assessment
3. Interviews (45 rounds)
4. Offer

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

【JAPAN AI】Agent Harness Engineer / English