AI Tech Lead — Context & Harness at MONEY FORWARD VIETNAM CO.,LTD

AI Tech Lead — Context & Harness at MONEY FORWARD VIETNAM CO.,LTD | ITviec
Tòa nhà E.Town Central, Số 11 Đoàn Văn Bơ, Xom Chieu, Ho Chi Minh

Skills:
Job Expertise:
Job Domain:
Software Products and Web Services
AI Software & Services
Top 3 reasons to join us
– Hybrid working; Flexible time; Macbook Pro
– L&D programs; 17 days off
– 13.7 million B2C users; 140,000+ business customer
Job description
Role overview
The Context & Harness Committee is moving from strategy to execution. We are seeking three hands-on AI Tech Leads to define and validate our context engineering standards and evaluation harness before company-wide rollout.
Core responsibilities
– Lead the technical design and delivery of the assigned Context and Harness workstreams
– Review context formats, ontology models, harness proposals, and integration approaches.
– Support pilot teams across MFBS divisions with context conversion, fixture authoring, and evaluation.
– Define how GitHub, Notion, and Slack content maps into the agent context engine.
– Establish guidelines for federated context routing and token-budget allocation across sources.
– Contribute to an ontology proof of concept covering one domain and one workflow using YAML-based schemas.
– Validate integration with internal or external agent platforms, including access and smoke testing.
– Capture pilot evidence and contribute to the standard and company best-practices package.
Your skills and experience
Required skills
Context engineering skills
– RAG and retrieval pipelines: Design advanced search and retrieval systems that supply models with accurate, relevant, and timely information.
– Context-window and token management: Compress conversation history, prioritize useful context, and filter noise to optimize model attention and cost.
– Memory architecture: Build short-term and persistent memory systems that allow agents to retain state across tasks and interactions.
– Instruction curation: Structure plain-English operating rules, Markdown files, domain guides, and other instructions for dynamic injection into agent tasks.
– Context formats and knowledge modeling: Design context layers and reusable knowledge assets using Markdown, YAML, schemas, and related formats.
Harness engineering skills
– Tool and MCP integration: Connect models to external APIs, execution environments, tools, and Model Context Protocol (MCP) servers.
– Sandboxing and permissions: Define safety boundaries, access controls, approval flows, and secure execution environments for agent actions.
– Validation and guardrails: Implement linters, automated tests, evaluations, and verification layers that detect hallucinations, policy violations, or broken code.
– Orchestration and control loops: Design multi-step workflows, retry logic, state transitions, and error-correction loops for long-horizon tasks.
– Evaluation harnesses: Build fixtures, benchmark tasks, quality gates, and CI-integrated evaluations using DeepEval, Promptfoo, or comparable frameworks.
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.
Success outcomes
– Context architecture, format, and evaluation standards are validated through representative pilots.
– Pilot teams can convert source knowledge and author evaluation fixtures using documented playbooks.
– A working repo-native harness POC demonstrates repeatable benchmark execution and quality gates.
– The committee has sufficient evidence to ratify the standard and decide whether to adopt or build the long-term harness.
Why you'll love working here
Caring Mental & Physical Recreation:
– Hybrid working
– Full salary in probation & 13th month salary
– Social insurance on full salary from probation
– Premium Health insurance from probation
– Flexible start 8AM-9AM from Mon-Fri
– 16 days off annually + 1 Birthday Leave
– Paternity leave extra 5 days
– Annual company trip; Quarterly team building activities
– Club activities
– Annual health check
Caring Career & Development:
– Clear Career path
– Foreign language & International technology-related certifications sponsoring
– Well-equipped facility: Macbook pro,  additional monitor,..
– Soft skill workshops
– Tech seminars
– Monthly and biannually Recognition Awards
– Performance review twice/year