Senior AI Engineer -

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 a Senior AI Engineer to own the architecture of the systems that advise real investment decisions on live financial data. This is not a role that takes a spec and implements it - you define how the agents think, set the technical bar for the team, and are accountable for correctness, reliability, and knowing when a model is wrong before it reaches a user.
What you'll do
Own the architecture of the multi-agent orchestration system end to end: intent classification, tool routing, sub-agent coordination, and response synthesis across complex financial queries
Design and evolve the memory architecture: user profile memory, session context, and long-term behavioral memory across conversations
Set the design standards for the tool library agents call - portfolio analyzer, market data fetcher, strategy backtester, scenario modeler, screener, report generator, compliance checker - and review other engineers' additions to it
Design and tune multi-tier LLM routing, optimizing quality, latency, and cost simultaneously across model tiers, with clear tradeoff criteria others on the team can follow
Architect RAG pipelines for financial documents: embedding strategy, vector search, retrieval quality evaluation, and grounded, source-cited responses at production scale
Integrate quantitative signals from the research team into agent reasoning - alpha factors, regime indicators, portfolio optimization outputs
Build and own the LLM evaluation framework: golden-set regression tests, hallucination detection, factual accuracy scoring against verified financial data - and define what "good enough to ship" means for the team
Build the observability stack for AI systems: token usage, latency per model tier, cost per conversation, and audit logs for compliance
Set technical direction for the AI engineering function, review architecture decisions, and mentor other engineers on production LLM system design
Requirements
4+ years building production AI or LLM-powered systems - shipped to real users at scale, not research projects
Track record owning the architecture of a non-trivial AI system from design through production, including the tradeoffs behind it
Deep, hands-on experience with LLM APIs in production: Claude, OpenAI, or Gemini - streaming, tool use, structured outputs, context window management
Agentic framework experience: LangChain, LangGraph, or equivalent - and the judgment to know when to build custom instead
Proven RAG pipeline experience: embedding models, vector databases, retrieval quality evaluation at production scale
Strong Python - async programming, clean architecture, testable code - and the ability to set code standards for others
Production mindset: you instrument everything, handle failures gracefully, and catch model errors before users do
Comfortable being the final technical decision-maker on AI architecture with minimal oversight
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 at scale: 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
Experience mentoring engineers or leading technical design reviews
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 AI Architect or Head of AI Engineering as XNO scales across SEA