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Bjak

Applied AI Engineer

United States (Remote)fulltimemidAdded 1 week ago

About this role

BJAK is hiring an Applied AI Engineer to build production AI systems for their neobank platform, focusing on real-world automation of financial workflows like onboarding, KYC, and customer support. This is a hands-on engineering role requiring experience shipping AI agents and LLM applications, not research-focused work.

What you'll do

  • Design and build AI agents, workflows, and automation tools that solve customer and operational problems in financial services
  • Implement LLM-based features including retrieval systems, tool calling, evaluations, and guardrails for production use
  • Develop AI-assisted experiences for onboarding, KYC, risk review, support automation, and document handling
  • Partner with product and engineering teams to identify high-impact automation opportunities and ship solutions quickly
  • Engineer reliable, scalable systems that handle constraints around accuracy, latency, cost, security, and compliance
  • Test, evaluate, and continuously improve AI systems in production to ensure quality and user trust

What they're looking for

  • Python, TypeScript, or JavaScript development
  • Building LLM applications, agents, and RAG systems
  • AI workflow automation and copilot design
  • Prompt engineering and model evaluation techniques
  • Production system reliability and testing
  • Fintech, banking, or operations automation domain knowledge
  • Risk, KYC, fraud, or support automation experience
  • Cross-functional collaboration with product and operations teams
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Bjak

Bjak is a Southeast Asian fintech super app offering insurance, payments, savings, wallets, and investment products through a unified platform. The company is hiring full stack engineers, backend engineers, iOS developers, and Android engineers to build scalable features and reliable systems across mobile and web products.

Website
bjak.com
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Likely interview questions

  • Walk us through an AI system you shipped to production—what was the workflow, what guardrails did you implement, and how did you measure reliability?
  • Describe a time when you had to decide whether a process should be automated with AI or handled manually. What was your reasoning?
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