AI/ML Engineer - HCM

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 backtester, 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