【JAPAN AI】Agent Harness Engineer / English
【JAPAN AI】Agent Harness Engineer / English
雇用形態 正社員
給与 年収 9,000,000 円 - 16,000,000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 (4–5 rounds)
4. Offer
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
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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 — a rule execution layer that controls LLM
output
● 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 QA Specialist — 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)
- 【JAPAN AI】Agent Harness Engineer / English
- Mission
- What Is an Agent Harness?
- Job Description
- Key Results (KRs / Metrics)
- Team Structure
- You May Be a Good Fit If You
- Strong Candidates May Also Have
- Tech Stack
- Learning & Development Support