JAPAN AI - Agent Harness Engineer

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

Hiring Process

1. Application Review
2. Coding Assessment
3. Interviews (4–5 rounds)
4. Offer

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