AI Tech Lead (Context & Harness), CTO Office

Overview

Money Forward is expanding AI agents across its products and internal workflows. Reliable agents need relevant context, controlled tool access, and repeatable evaluation.

Within the CTO Office, the Context & Harness Committee develops and validates shared standards for context engineering—the information agents receive—and harness engineering—the execution controls, tool integrations, and evaluations supporting safe, reliable operation.

We are seeking a hands-on AI Tech Lead to lead assigned workstreams, build and test solutions with pilot teams, and turn proven approaches into reusable components and standards for wider adoption.

Responsibilities and Duties

You will combine hands-on engineering with cross-team technical influence, owning the implementation, evaluation, and documentation of your assigned workstream.

Working with committee leadership, pilot teams, and platform and security specialists, you will:

• Lead technical design and delivery for assigned context-engineering or harness-engineering workstreams, clarifying requirements and architecture tradeoffs.
• Review context formats, ontology models, harness designs, and integration approaches.
• Support pilot teams across Money Forward Business Company divisions with context conversion, test fixtures, and evaluation.
• Define how GitHub, Notion, Slack, and other sources supply agent context, including routing and token-budget allocation.
• Build a focused ontology proof of concept using YAML-based schemas.
• Validate agent-platform integrations, including access controls and smoke tests, and demonstrate repeatable evaluation and quality gates.
• Turn pilot evidence into reusable components, playbooks, and recommendations for company-wide standards and adoption.

Expected Mindset

We value rapid hypothesis testing, continuous learning, and technical decisions grounded in product value. You should share findings openly, collaborate constructively, and balance experimentation with reliability, security, compliance, and cost.

Required Skills and Experience

Candidates should demonstrate depth in either context engineering or harness engineering, with working knowledge of the other. The areas below describe the skills relevant to each specialization; candidates are not expected to have equal depth across both.

• Context engineering skills
  • RAG and retrieval pipelines: Experience designing advanced search and retrieval systems that provide models with accurate, relevant, and timely information.
  • Context-window and token management: Experience compressing conversation history, prioritizing useful context, and filtering noise to optimize model attention and cost.
  • Memory architecture: Experience building short-term and persistent memory systems that allow agents to retain state across tasks and interactions.
  • Instruction curation: Experience structuring plain-English operating rules, Markdown files, domain guides, and other instructions for dynamic injection into agent tasks.
  • Context formats and knowledge modeling: Experience designing context layers and reusable knowledge assets using Markdown, YAML, schemas, and related formats.
• Harness engineering skills
  • Tool and MCP integration: Experience connecting models to external APIs, execution environments, tools, and Model Context Protocol (MCP) servers.
  • Sandboxing and permissions: Experience defining safety boundaries, access controls, approval flows, and secure execution environments for agent actions.
  • Validation and guardrails: Experience implementing linters, automated tests, evaluations, and verification layers that detect hallucinations, policy violations, or broken code.
  • Orchestration and control loops: Experience designing multi-step workflows, retry logic, state transitions, and error-correction loops for long-horizon tasks.
  • Evaluation harnesses: Experience building fixtures, benchmark tasks, quality gates, and CI-integrated evaluations using DeepEval, Promptfoo, or comparable frameworks.

Preferred Skills and Experience

• Supporting technical and leadership skills
• LLM and agent fundamentals: Strong understanding of agent workflows, RAG, evaluation methods, and quality gates for AI-generated output.
• Software engineering: Strong Git/GitHub and CI practices, experience with repo-native tooling, and the ability to prototype quickly.
• Technical leadership: Ability to make pragmatic architecture decisions, lead POCs, and turn evidence into reusable standards.
• Communication: Ability to write clear technical proposals and provide constructive cross-team review and feedback.
• Experience with ontology or knowledge modeling using YAML-based schemas.
• Familiarity with internal or external agent platforms.
• Experience with hybrid search, indexing pipelines, or context freshness mechanisms.
• Experience in AI development and/or experience in using AI tools to improve development processes.

Money Forward is at a major turning point, shifting "from Cloud to AI." We are currently driving "AX (AI Transformation)"—the next step beyond DX—with the goal of providing "Digital Workers," where AI agents autonomously execute tasks. As we enter a phase of evolving into Japan's No. 1 back-office AI company by integrating AI agents into all of our products in the future, we are looking for individuals who can contribute to AI-driven development and value creation.

Language Requirements

English: Business Level (Fluent, capable of handling communication with clients in English)
(Note: If you have other qualifications or experiences demonstrating English proficiency, such as EIKEN Pre-1, EIKEN 2nd Grade (CSE score 1950+), TOEFL iBT 60+, IELTS 5.0+, or Cambridge FCE.), feel free to discuss with us)
For those without a TOEIC 700+ equivalent score, they will be asked to take a designated test during the interview process (generally after the first interview)

Japanese: N3 Level (Able to communicate internally using translation tools for text and conversation)

Who We’re Looking For

• A hands-on technical leader who enjoys turning ambiguous problems into working, testable solutions.
• Someone who can work across teams without relying solely on formal authority.
• Someone who can distinguish a successful demonstration from a reliable, reusable engineering solution.
• Someone who documents tradeoffs and limitations clearly and helps other teams adopt what works.

Tools Used

• Repository management: GitHub
• CI and evaluation automation: GitHub Actions
• Collaboration and documentation: Slack, Notion
• Agent and evaluation tooling: selected according to workstream and pilot requirements

Work Environment

At Money Forward, we provide an environment where we can create world-class services together, and we are looking forward to welcoming you.

• Provided PC Specs: We provide PCs equipped with the latest CPUs (MacOS or Windows). Custom-made PCs tailored to business requirements and replacements with the latest OS are also possible.
• Systems to Enhance the Development Environment: Peripheral devices necessary for work (such as displays, mice, keyboards) can be purchased as office supplies. Generally, you can choose from standard products (catalog), and if conditions are met, you can apply for non-standard products as well. (*Excluding certain group companies)
• Money Forward Library: We have a library system where you can freely borrow books, ranging from technical books to management books. Desired books can be purchased at the company's expense.
• Referral Driven: We cover the cost of recruitment meals. There is a referral reward system.
• Conference Participation Support: The company partially covers participation in domestic and international conferences, such as RubyKaigi and Google I/O.