AI Jobs in Web3

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Job Position Company Posted Location Salary Tags

Crypto.com

Hong Kong, Hong Kong

$105k - $150k

Zscaler

Remote

$112k - $154k

Zinnia

Remote

$126k - $131k

Zinnia

Remote

$106k - $117k

Zinnia

Remote

$130k - $150k

Gsrmarkets

Remote

$103k - $112k

Bitpanda

Remote

$93k - $149k

Kronosresearch

Remote

$121k - $125k

Tether

Sao Paulo, Brazil

$90k - $150k

Tether

Islamabad, Pakistan

$90k - $150k

Tether

Bangalore, India

$90k - $150k

Tether

Singapore, Singapore

$90k - $150k

Tether

Beijing, China

$90k - $150k

Tether

Paris, France

$90k - $150k

Tether

Stockholm, Sweden

$90k - $150k

Crypto.com
$105k - $150k estimated
Hong Kong, Hong Kong SAR
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Trading Ops & Risk Systems Engineer (AI & Automation)

Hong Kong, Hong Kong SAR
Trading – Quant Middle Office /
Full-time /
Hybrid

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We are seeking a highly analytical and technical professional to spearhead the evolution of our trading infrastructure.  This hybrid role combines the fast-paced execution of a Trading Operations Specialist, the analytical rigor of a Risk Manager, and the technical capabilities of an AI-focused Developer.

The successful candidate will not only manage daily operational workflows and risk monitoring but will also lead the digital transformation of these functions. You will leverage Large Language Models (LLMs), machine learning, and advanced scripting to automate legacy manual processes, improve predictive risk modeling, and build real-time P&L visualization tools.

Key Responsibilities:

    • 1. Trading Operations and P&L Analysis with AI
    • Manage the end-to-end lifecycle of trades across multi-asset classes.  Develop agentic AI workflows using Python (ML) and MLOps tools to automate manual operations and reconciliations
    • Perform daily P&L attribution and explain variance by decomposing market moves, Greeks, and new activity. Leverage AI/ML frameworks (e.g., OpenAI API, LangChain, or local LLMs) to build intelligent agents for anomaly detection in trade data and automated commentary generation for P&L reports.
    • Ensure data integrity across trading systems, middle-office platforms, and downstream finance ledgers.
    • 2. Risk Monitoring & Control
    • Intelligent Risk Management: Develop real-time, AI-driven risk models that use machine learning to predict market volatility and alert the desk to emerging cross-market risks.
    • Develop and maintain sophisticated risk reporting dashboards for the desk.

Requirements & Qualifications

    • Experience: 5+ years in front-office operations, quant development, or a blend of risk management and systems architecture within a financial institution.
    • Technical Stack: Expert proficiency in Python (for AI/ML), and strong SQL skills. Experience building agentic AI workflows is a significant advantage.
    • Financial Acumen: Deep understanding of P&L attribution, financial controls, market risk indicators (Greeks, VaR), financial markets and products.
    • Problem Solving: A "systems-thinking" approach to operations—treating every manual task as a bug that needs an automated solution.
    • Capacity: Ability to thrive in a high-intensity environment requiring extended hours (eg. 60-70/week) to meet global trading demands.
    • Agility: Willingness to participate in a rotational weekend and 24/7 on-call schedule, providing immediate resolution to live production issues
We may use artificial intelligence tools to analyze the content of your Resume/CV against the specific requirements for the position. The purpose is to support our recruitment team in reviewing applications more effectively. These tools assist our recruitment team in their evaluation of your application by providing recommendations, but they do not replace human judgment. Final hiring decisions are ultimately made by humans who consider the insights generated by the tools along with other relevant information. If you would like more details about how your personal information is processed, please contact us.
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What an AI Developer does?

An AI developer is someone who creates and builds artificial intelligence systems

Their responsibilities may include designing and implementing algorithms, creating and training machine learning models, and deploying AI systems to solve practical problems

Additionally, they may be responsible for maintaining and improving existing AI systems, as well as collaborating with other teams or individuals to integrate AI technology into larger systems.