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# Applied AI Engineer - AI Neobank App (Austria)

- **Company:** Bjak
- **Location:** Austria (Remote available)
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Business Systems, Python (Programming Language), Software Engineering, Systems Integration, Large Language Models, Backend
- **Published:** July 15, 2026
- **Apply:** https://www.adzuna.at/details/5796272323

## About the Role

* Strong software engineering foundation, preferably with Python and backend systems.
 * Hands-on experience building with LLM APIs, agents, RAG, workflow automation or AI tools.
 * Able to connect AI systems with real product, data and operational workflows.
 * Good judgement on where AI helps and where rule-based systems or human review are better.
 * Understands evaluation, accuracy, latency, cost, privacy and failure modes.
 * Fast builder who can prototype, test and ship practical systems.
 * Experience in fintech, insurance, support automation, CRM or operations automation is a strong advantage.

The Kind of Builder We Want

 * Practical AI builder, not just a prompt experimenter.
 * Thinks in workflows, systems and measurable quality.
 * Can build quickly but still cares about guardrails and reliability.
 * Comfortable working with messy real-world data and processes.
 * Honest about what AI can and cannot do., English is our main working language across global teams. Strong English communication is required.

## Description

We are looking for applied AI engineers to build AI-native systems for BJAK's AI Neobank app.

This is not a research-only role. We need builders who can use AI to automate real workflows, improve products, reduce manual work and make financial services easier for users and teams.

What You'll Own

 * Build AI-powered workflows, assistants, agents and automation systems.
 * Apply AI across customer support, CRM, onboarding, claims, renewals, payments, operations and internal tools.
 * Work with product and engineering teams to turn manual processes into scalable AI-native systems.
 * Build integrations with LLMs, internal data, APIs, documents, knowledge bases and business systems.
 * Design evaluation, monitoring and fallback flows so AI outputs are useful, safe and reliable.
 * Prototype quickly, test with users or operators, then productionize what works.
 * Improve speed, quality and consistency across workflows using AI where it creates real business value., * People who only want research projects with no production ownership.
 * Candidates who treat AI as demos instead of working systems.
 * Engineers who do not care about evaluation, reliability or user impact.
 * People who overclaim AI impact without evidence.
 * Builders who cannot work with product, ops and business teams.


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