XNO is a quantitative AI technology company building institutional-grade financial infrastructure for professional investors across Vietnam and Southeast Asia. We operate at the intersection of systematic trading, AI, and financial data - serving fund managers, trading desks, and wealth professionals with tools they actually depend on for live decisions.
We're looking for an
AI Engineer who builds production AI systems - not prototypes. What you ship here advises real investment decisions and runs on live financial data. Correctness, reliability, and the ability to know when a model is wrong are not optional.
What you'll do
Design and build the multi-agent orchestration system: intent classification, tool routing, sub-agent coordination, and response synthesis across complex financial queries
Implement and maintain the memory architecture: user profile memory, session context, and long-term behavioral memory across conversations
Build and maintain the tool library that agents call: portfolio analyzer, market data fetcher, strategy back
tester, scenario modeler, screener, report generator, and compliance checker
Design multi-tier LLM routing: optimize for quality, latency, and cost simultaneously across different model tiers
Build RAG pipelines: embedding financial documents, vector search, and context retrieval for grounded, source-cited responses
Integrate quantitative signals from the research team into agent reasoning - alpha factors, regime indicators, portfolio optimization outputs
Build LLM evaluation frameworks: golden-set regression tests, hallucination detection, factual accuracy scoring against verified financial data
Build the observability stack for AI systems: token usage, latency per model tier, cost per conversation, and audit logs for compliance
Requirements
2+ years building production AI or LLM-powered systems - shipped to real users, not research projects
Deep familiarity with LLM APIs in production: Claude, OpenAI, or Gemini - streaming, tool use, structured outputs, context window management
Agentic framework experience: LangChain, LangGraph, or equivalent - you know when to use them and when to build your own
RAG pipeline experience: embedding models, vector databases, retrieval quality evaluation
Strong Python - async programming, clean architecture, testable code
Production mindset: you instrument everything, handle failures gracefully, and know when the model is wrong before users do
Bonus points
Experience in fintech or financial services AI: compliance constraints, explainability requirements, data sensitivity
Knowledge of Vietnamese financial products: mutual funds, VN30F derivatives, equities, bonds
Multi-agent system design: agent handoffs, tool orchestration, parallel sub-agent execution
LLM evaluation and red-teaming for factual accuracy in high-stakes domains
Familiarity with financial data sources: market data feeds, fund NAV series, macro indicators
Prior experience with goal-based financial planning or robo-advisory systems
Benefits
Competitive salary + performance bonus
Full ownership of AI architecture decisions - you define how the agents think
Direct collaboration with PhD-level quant researchers and institutional finance professionals on live systems
Work on AI systems where domain depth is the moat - not just prompt engineering
Clear growth path to Lead AI Engineer or AI Architect as XNO scales across SEA