Technical Product Manager

About BJAK

The original mission of BJAK is we believe people deserve smarter ways to plan, save and grow their money. This is the origin of our name.

Started in 2019, we built the first mobile-first, insurance platform, enabling insurance to be accessible online by millions in the region. Today, its the leading insurance platform in Southeast Asia.

Today, we are expanding ways to help people in the region — this includes spending, saving, investing, exchanging, travelling, and more. Our mission is help people get more from their money every day.

We have teams working around the world, with over 20 nationalities from our offices and remotely, who truly enjoys their work. We are looking for the most talented and driven people we can find. We are looking for people who work for their passion, not counting hours. Who loves building great next-generation products, not status quo. Who cares about redefining how everyone around us can get the best financial applications, not for an exclusive few.

If you're this person, we'd love to talk to you.

The Role

BJAK is building a Neobank Superapp for Southeast Asia. As a Technical Product Manager, you’ll own a product vertical end to end—such as payments and expense management, remittance, insurance, or wealth and trading—from strategy, outcomes, and roadmap through to the technical design and delivery of the systems behind it.

This is a product where real money moves, mistakes are immediately visible, and licensing, compliance, and third-party rails are core product constraints—not considerations around the edges.

We are AI-native by default, but AI is a means, not the goal. The bar is knowing where a model can genuinely improve an outcome, where deterministic systems must remain in control, and how to build products that are reliable, auditable, and trusted when money is involved.

What you'll be doing

• Own a vertical end-to-end — outcomes and metrics, a backlog prioritised by value / risk / dependency / strategic priority, acceptance criteria crisp enough to build and test against.

• Shape the technical design with engineering — system boundaries, money‑movement correctness (idempotency, ledgering, reconciliation), third‑party‑rail reliance and rail‑down behaviour, failure/retry/refund paths, security, latency, scalability, recovery.

• Make hard trade‑offs across correctness, speed, cost, reliability and UX — and defend them to an engineering, compliance or ops lead who pushes back.

• Treat regulated constraints as first‑class product inputs — licensing scope, CDD/KYC and limits, sanctions/PEP screening, transaction monitoring, data residency and privacy. Know which are hard launch gates and which are forward‑readiness.

• Apply AI where it earns its place — define what it’s allowed to decide, where a human stays in the loop, how it’s evaluated, and what it does when unsure. Every AI‑surfaced number traces back to a real record.

• Ship against external gates — build feature‑complete behind flags; go live per rail as each partner and regulatory gate clears.

• Work across Engineering, Design, Compliance, Operations, Treasury and Finance/Reconciliation.

What you'll need

• Product leadership — significant experience owning a complex technical product end to end; strong judgement and decisiveness under ambiguity; ability to balance ambition against technical, regulatory and operational reality; can hold their own with senior engineers.

• Technical foundation — solid system design (boundaries, data stores, queues/workflows, APIs, failure modes, consistency under concurrency); comfort reading, reviewing and challenging design docs; can sketch an architecture and a sequence. No production code, but technical enough that engineers don’t have to simplify.

• Domain
  — experience in a regulated / high‑consequence domain (payments, banking, remittance, insurance, …) or demonstrable fast‑ramp ability; understands why money movement is different (correctness over convenience, irreversible states, partners you don’t control).

• AI judgement — hands‑on with LLM‑based products; can frame a feature as eval‑able (offline, online, human feedback); realistic on hallucination/drift/data gaps, including data‑blocked vs…