Tuyển dụng Product Engineer Insurtech SaaS, Golang, AI tại Hà Nội

About the job

Product Engineer

• AI-first engineering, uncompromising fundamentals. Accountable for the outcome, not the output.
• Full-time · On-site · Flexible working hours · Distributed, multi-timezone team · AI-first engineering · 3+ years experience
• The role is remote from your start date and becomes hybrid when our new office opens, expected around 20 December

Job Summary

We are hiring a Product Engineer - an engineer accountable for outcomes, not output.

We do not measure engineers by tickets closed or lines shipped. We measure them by whether the thing they built actually solved the user's problem. That means you will be expected to question requirements before building them, own a feature from ambiguity through to production behaviour, and stay attached to it after release - watching the metrics, the logs, and the support tickets.

You will work with Product Managers, designers, and business stakeholders based in different countries and timezones. Overlap hours are limited by design, so clear communication and the ability to move a decision forward asynchronously - without waiting for a meeting - matter as much as your engineering skill.

This is a deeply technical role with an AI-first mindset. AI coding agents are the default way we build here - you will spend more of your day designing architecture, steering agents, and reviewing generated code than typing implementations by hand. We expect fluency with spec-driven development, reusable agent skills, and context engineering, not occasional autocomplete use. Which is exactly why fundamentals and best practices matter more, not less, so you will be held to a high bar on SOLID, Clean Architecture, design patterns, distributed system design, relational data modelling, and automation testing: these are working knowledge, not résumé keywords, and you should expect to defend your choices reasonably in code review and in the interview.

You remain the author of record for everything that lands in main. "The agent wrote it" is never an acceptable explanation for a defect.

Key Responsibilities

• Own the outcome. Take a business problem from vague to shipped to measured. Define what success looks like with your Product Manager, instrument for it, and act on what the data says - including rolling back or rewriting your own work when the numbers disagree with the plan.
• Find the problem, not just the solution. Interrogate requirements before implementing them. Surface the cheaper path, the edge case nobody scoped, and the feature that shouldn't be built. Say so early and in writing.
• Collaborate across borders and timezones. Partner directly with Product Managers, designers, QA, and business stakeholders distributed across multiple countries. Write decisions down, keep specs and PR descriptions self-explanatory, unblock people in other timezones, and run the occasional early or late call when the others need it.
• Architect and build services and APIs. Design RESTful (and where appropriate, event-driven or gRPC) services applying SOLID, Clean/Hexagonal Architecture, and design patterns deliberately - with a clear justification for the boundaries you draw. Design normalised, indexed, migration-safe relational schemas that hold up under real query load. Architecture is the part an agent cannot infer for you: you set the boundaries and the contracts, then let agents fill them in.
• Design for distribution. Make grounded calls on monolith vs. microservices. Build services that satisfy 12-factor principles - externalised config, stateless processes, disposability, dev/prod parity - and containerise them for Docker/Kubernetes deployment.
• Automate the safety net. Own automated testing for what you build: unit, integration, contract, and end-to-end where it earns its keep. Tests ship in the same PR as the code, not in a follow-up ticket. A bug fix without a reproducing test is not a fix. In an AI-first workflow the test suite is the specification - it is what makes agent-generated change safe to merge at speed.
• Work AI-first, and hold the line on quality. Drive delivery with coding agents, know your acceptance criteria first, let agents execute, then review hard. Build and maintain reusable agent skills, project instructions, and guardrails so the whole team gains leverage - not just your own throughput. Reject output that is merely plausible: over-abstracted layers, duplicated logic, silently swallowed errors, tests that assert nothing. Never merge code you cannot explain line by line.
• Run what you ship. Instrument services with structured logging, metrics, and tracing. Debug production incidents, write the postmortem, and close the loop with a fix that prevents recurrence.
• Raise the team's bar. Review peers' PRs substantively, document trade-offs and decisions, and mentor on fundamentals and on responsible AI-assisted workflows.

Job Requirements

• 3+ years building and operating production backend services - and you can walk through a system you designed, why you designed it that way, and what you'd change now.
• Deep, demonstrable command of software design fundamentals: OOP, SOLID, Clean Architecture, and design patterns. You will be asked to critique a design and explain what breaks at scale. This is the non-negotiable core of the role - in an AI-first team, fundamentals are the review capability that keeps agent velocity from becoming agent debt.
• Strong RESTful API design: resource modelling, versioning, idempotency, pagination, error contracts, authentication/authorisation.
• Strong relational database design (PostgreSQL/MySQL): normalisation and when to break it, indexing strategy, transactions and isolation levels, query analysis, safe schema migrations.
• Microservices and 12-factor apps: service boundaries, inter-service communication, failure isolation, configuration and secrets management - plus honest judgment on when a monolith is the correct answer.
• Automation testing discipline: you write tests first or alongside, understand the test pyramid, and use test doubles without mocking away the thing under test. You treat coverage as a floor for confidence, never as a number to satisfy - and you can tell a genuine regression test from an agent-generated one that asserts nothing.
• Containerisation: confident with Docker; working knowledge of Kubernetes (deployments, services, config/secrets, health probes, resource limits).
• An AI-first way of working, already in your hands: real production experience with Claude Code, Cursor, Copilot, or equivalent - including spec/context engineering, agent skills or custom commands, and a disciplined self-review process before any PR. You should be able to describe a time an agent produced plausible-but-wrong code, how you caught it, and which fundamental (a design principle, a data-model rule, a failing test) let you catch it.
• Product mindset with real ownership instincts: comfortable with ambiguity, able to talk to non-engineers about trade-offs in their language, and biased toward shipping something small to learn rather than debating something large.
• Effective asynchronous, cross-cultural collaboration: professional written English, the discipline to