Tech Lead AI Product Engineering

About this role

What is Cobre, and what do we do?

Cobre is Latin America’s leading instant b2b payments platform. We solve the region’s most complex money movement challenges by building advanced financial infrastructure that enables companies to move money faster, safer, and more efficiently.

We enable instant business payments—local or international, direct or via API—all from a single platform.

Built for fintechs, PSPs, banks, and finance teams that demand speed, control, and efficiency. From real-time payments to automated treasury, we turn complex financial processes into simple experiences.

Cobre is the first platform in Colombia to enable companies to pay both banked and unbanked beneficiaries within the same payment cycle and through a single interface.

We are building the enterprise payments infrastructure of Latin America!

The team you'd lead:

Risk Products builds the systems that decide who Cobre can do business with and which money movements are allowed to happen: client onboarding and KYB, document collection and verification, sanctions and counterparty screening, real-time transaction decisioning, and the backoffice where compliance analysts do their work.

The team runs the Builder Model — engineers own product, design and code end-to-end, talking directly to compliance and operations rather than consuming a backlog someone else wrote. And it is AI-native on both sides: LLMs do real production work in our onboarding and evidence flows, and the team builds with a shared AI toolkit, company-wide MCP servers and custom agents and skills our engineers write for their own workflows.

These are strengths, and they are also the hard part. A team with this much autonomy needs someone holding the technical line: coherent architecture across services, contracts other teams can build on, and a shared standard for what "good" means when part of your system is a language model.

What we are looking for:

A hands-on technical leader for a squad building AI-powered products in a regulated domain.

You will set technical direction, own the architecture, grow the engineers around you, and be accountable for what the team delivers and how well it runs in production — while still writing code on the problems that most need your judgement. This is a hands-on technical leadership role, not a step out of engineering, and depending on how the squad is composed it may include direct reports.

What would you be doing:

Technical direction

• Own the architecture for your squad's services and how they fit the wider decisioning and screening platform — including the API contracts and SLAs other teams depend on.

• Make and document the consequential calls: build vs. adopt, model and provider selection, where a boundary belongs, when to pay down debt and when to live with it. Write the ADR; be specific about the trade-off you accepted.

• Decide where AI belongs, and kill the cases where it doesn't. Your team will sometimes propose a model-shaped solution to a problem a deterministic rule solves better and cheaper. Saying no to those with a reason is core to this job — as is owning the cost and latency envelope of the ones you say yes to.

• Set the standard for how the team builds with AI: what has to be true before an AI feature ships, how correctness is defined and measured, where the human stays in the loop, how prompts, models and thresholds are versioned and reviewed, what gets logged for audit, and what the system does when the model is wrong or the provider is down.

• Keep vendors, SDKs and models at the edge behind adapters, so switching one is a contained change rather than a project.

Delivery

• Turn ambiguous product and regulatory problems into a sliced, sequenced plan the team can execute — and be honest about scope when it doesn't fit.

• Own delivery outcomes: what shipped, whether it worked, and the metric that says so. Instrumentation is part of the definition of done.

• Run incidents for your domain and make sure the follow-ups actually land.

• Partner with product, compliance, operations and other engineering leads as a peer, including saying no with a reason.

People

• Mentor the engineers on your team — pair on the hard parts, review with substance, and deliberately grow people into owning features end-to-end. This is the part of the role we will hold you to hardest; a squad that only moves as fast as its lead is a failed squad.

• Give feedback that is specific and timely, and have real conversations about career direction.

• This role may carry direct reports. Depending on how the squad is composed, you may formally manage part of the team — performance conversations, growth plans and career progression. Either way you are accountable for the development of the engineers around you.

• Hire — define the bar, run interviews, and onboard people so they ship something meaningful in their first weeks.

• Multiply AI adoption across the team: the skills, guardrails, evals and toolkit contributions that make ten engineers faster, not just you — and hold the line that AI-produced code meets the same bar as anything else the team merges.

• Build a culture where engineers own product decisions and are comfortable talking to a compliance analyst directly.

Hands-on

• Stay in the code — roughly a third of your time, on the architecturally load-bearing work, the risky migrations and the reviews that need your context. You should be the person others want reviewing their design.

What do you need:

• 7+ years in software engineering, including time owning the technical direction of a team or a substantial domain and being accountable for its delivery.

• Deep backend expertise in at least one back-end focused language, and the willingness to learn Go if you don't already know it — you will be setting standards in it. Our stack is Go-first, with some Node/TypeScript and Vue 3 in the mix.

• Production experience leading LLM-backed product work — you have shipped it, evaluated it, watched it…