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

Application Review

Coding Assessment

Interviews (4–5 rounds)

Offer

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