Applied AI Engineer - AI Finance Agent
Bjak · Remote · China ·
COMPENSATION
€•• – ••k
Listed on the employer's job board (Ashby)
- Fully remote (China only)
- Python + LLM APIs
- AI Workflow Automation
- Fintech & Insurance
- Fast hiring process
You will join BJAK, a leading insurance platform in Southeast Asia, to develop practical AI solutions for their AI Finance Agent. The team aims to automate manual processes across customer support, claims, and operations, turning them into scalable, reliable systems.
This position requires strong software engineering skills combined with hands-on experience in building AI agents and workflow automation. You will work closely with product and engineering teams to integrate LLMs with internal data and business systems, ensuring safe and effective deployment.
What you'll do
- Build AI-powered workflows, assistants, agents, and automation systems.
- Apply AI to customer support, CRM, onboarding, claims, and payments.
- Integrate LLMs with internal data, APIs, documents, and knowledge bases.
- Design evaluation, monitoring, and fallback flows for reliable AI outputs.
- Prototype quickly, test with users, and productionize successful systems.
- Collaborate with product and engineering to scale AI-native solutions.
What you'll need
- Strong software engineering foundation, preferably with Python.
- Hands-on experience with LLM APIs, agents, RAG, and workflow automation.
- Ability to connect AI systems with real product and operational workflows.
- Understanding of evaluation, accuracy, latency, cost, and privacy.
- Strong English communication skills for global team collaboration.
- Must be currently based in China.
Nice to have
- Experience in fintech or insurance sectors.
- Background in support, CRM, or operations automation.
- Judgement on when to use AI versus rule-based systems.
Stack
- Python
- LLM APIs
- RAG
- Backend Systems
How you'll work
- Fully remote role
- Candidates must be based in China
- English is the main working language
Hiring process
- Online assessment or practical task
- Technical interview
- CEO or final round interview
- Targeting offer within one week for strong candidates