AI Product Engineer (FinTech)

The Role
Our standard delivery model splits work across a business analyst, a system analyst and team lead, developers, and testers. We are looking for a rare engineer who can own that entire cycle end to end — gathering requirements, shaping the technical solution and specification, building it across backend, frontend, and mobile, and testing it properly — using modern AI tooling as the force multiplier that makes this possible.

This is not a "prompt engineer" role and it is not a traditional full-stack role. It is a full-cycle builder who has genuinely internalized AI-assisted development: someone who uses coding agents, LLM APIs, and AI-driven testing daily to compress what used to take a team into what one strong, AI-fluent engineer can deliver — without sacrificing quality, security, or maintainability.

You will work directly with the CTO and product stakeholders, take an idea from a vague business need to a shipped, observable, production-grade feature, and set the standard for what AI-native engineering looks like at Billease.

WHAT YOU'LL DO:
Discovery & requirements
- Engage business stakeholders directly to elicit, clarify, and document business requirements.
- Turn ambiguous problems into clear, testable requirements — using AI to accelerate research, drafting, and gap analysis.

Solution design & specification
- Produce technical solutions and specifications: data models, API contracts (OpenAPI), service boundaries, sequence and architecture diagrams.
- Make sound architecture decisions aligned with our microservices, event-driven, and security-first principles, and document the trade-offs.

Build (backend / frontend / mobile)
- Implement backend services in Python and/or Golang on PostgreSQL, with appropriate messaging (Kafka) and caching.
- Build web frontends (Vue/Nuxt or React) and, where needed, mobile features (Android/Kotlin, iOS/Swift).
- Write reusable, testable, efficient code, using AI coding agents to move fast while keeping the codebase clean and reviewable.

Test & quality
- Design and execute meaningful test coverage — unit, integration, API, and end-to-end — leveraging AI-assisted test generation and review.
- Own quality end to end: you are the developer *and* the tester, and "done" means verified.

Ship & operate
- Deliver through our CI/CD pipelines and infrastructure as code.
- Build observability in at design time (metrics, logs, tracing) and support what you ship.

MUST-HAVE QUALIFICATIONS:
Mandatory: demonstrable, hands-on experience with a range of AI tools in real development work. This is the non-negotiable core of the role — we will ask you to show concrete examples of what you have built and how AI changed your workflow and output.

Real, current experience with AI development tooling, such as:
- AI coding agents / assistants (e.g. Claude Code, Cursor, Copilot, or similar) used to deliver production work, not just experiments.
- Working directly with LLM APIs (e.g. Anthropic, OpenAI) — prompt design, structured outputs, tool/function calling, and agentic workflows.
- AI-assisted testing, code review, documentation, and requirements/spec generation.

Full-cycle software engineering experience — you have personally taken features from requirement to production, not only implemented tickets handed to you.
- Backend proficiency in Python and/or Golang, with solid PostgreSQL and SQL skills.
- Frontend capability with a modern framework (Vue/Nuxt or React).
- Ability to write clear specifications and communicate effectively with both technical and non-technical stakeholders.
- Understanding of microservices, REST/API design, event-driven patterns, Git-based workflows, and CI/CD.
- Security- and quality-conscious mindset; comfortable owning testing and operability of your own work.
- Self-directed, high-ownership, and able to deliver under real deadlines.

NICE-TO-HAVE
- Fintech / financial services experience — lending, payments, digital banking, or regulatory-aware development (a strong plus).
- High-load / high-scale systems experience — performance tuning, partitioning, caching strategies, and designing for reliability under load.
- Mobile development experience (Android/Kotlin, iOS/Swift).
- Cloud-native and DevOps exposure: AWS, Kubernetes, Terraform/Ansible, Kafka, and observability tooling (Prometheus, Grafana).
- Experience integrating AI features into products (chatbots, voice, document/image processing, RAG, or automation).

HOW WE WORK
- Stack: AWS, Kubernetes, Python & Golang, PostgreSQL, Kafka, Vue/Nuxt & React, Android/iOS, Terraform/Ansible, GitLab CI/CD.
- Principles: clear and maintainable architecture, security-first, scalability and reliability, high observability and automation, cloud-native and microservices-oriented design, and a strong focus on performance, resilience, and operational excellence.
- AI-native by default: we expect AI to be part of how you think, design, build, and test — used deliberately and responsibly.

WHAT SUCCESS LOOKS LIKE:
Within your first few months, you independently take a real business need through the full cycle — requirements, spec, implementation across the relevant layers, tests, and a production release with observability — at a speed and quality that would traditionally have required a small team, and you help the rest of engineering raise its AI fluency along the way.