---
title: 'Machine Learning Engineer Lead, Vulcan (Global) at AIFT'
canonical: 'https://feeny.ai/job/machine-learning-engineer-lead-vulcan-global-aift-taipei-40dxp5392ey1'
type: 'job'
last_seen: '2026-09-14'
---

# Machine Learning Engineer Lead, Vulcan (Global) at AIFT

- **Company:** AIFT
- **Location:** Taipei, Taiwan / Hong Kong / Singapore / Japan / Abu Dhabi, United Arab Emirates
- **Posted:** 2026-03-06
- **Last confirmed live:** 2026-09-14
- **Apply:** https://job-boards.greenhouse.io/aift/jobs/5820463004

## Job description

## About the role

We are seeking an experienced Machine Learning Lead to helm our Machine Learning team.

In this pivotal role, you will be the engineering architect behind Vulcan’s core AI capabilities. You will act as the nexus between Research, Platform, and Product. Your mission is to translate cutting-edge findings on GenAI threats into robust, production-ready machine learning models that power our GenAI Security Guardrails (Blue Team) and Automated Vulnerability Assessment (Red Team).

Crucially, you will serve as the bridge between deep tech and business strategy, articulating technical constraints (like FLOPS and latency) to leadership and clients while guiding the engineering direction.

## Key Responsibilities

1. Model Development & Optimization (Training & Fine-tuning):

- Research to Production: Collaborate with the Security Research Team to operationalize new threat detection techniques. They identify the "what" (e.g., new prompt injection patterns); you determine the "how" (model architecture, training strategy).
- Fine-tuning & Adaptation: Lead the fine-tuning of Language Models (e.g., using LoRA/PEFT) to optimize for our supported muti-lingual languages and specific security intents.
- Multimodal Readiness: Prepare the system for Multimodal (Text + Image/Audio) capabilities. Evaluate and implement models to detect visual prompt injections and non-textual threats as the product evolves.

1. MLOps& Data Infrastructure:

- Enhance & Scale MLOps: Take ownership of our existing ML pipelines. Focus on optimizing and scaling CI/CD/CT workflows to improve training efficiency and deployment velocity.
- Data Governance: Implement and enforce rigorous Data Versioning strategies (e.g., DVC) to ensure complete reproducibility of model artifacts and datasets.
- Monitoring & Reliability: Maintain rigorous monitoring for model drift and performance, ensuring high reliability in a production security environment.

1. Cross-Functional Implementation & Leadership:

- Platform Collaboration: Work closely with the Platform Engineering Team to integrate ML models into the broader product architecture. Ensure seamless interaction between model inference services and the main platform logic.
- Team Leadership: Lead and mentor Machine Learning Engineers, fostering a culture of engineering rigor, code quality, and operational excellence.
- Resource Management: Manage GPU resources and compute budgets effectively for both training and inference workloads.

1. Technical Strategy & Stakeholder Management:

- Translating Tech to Business: Act as the technical voice of the ML team. You must effectively explain complex ML concepts (e.g., FLOPS, quantization trade-offs, model latency vs. accuracy) to executive leadership and clients.
- Cost-Benefit Analysis: Justify compute resource investments. Articulate the trade-off between infrastructure costs (GPU hours) and performance gains to non-technical stakeholders.

## Qualifications

- Experience: 5+ years in Machine Learning Engineering, with specific experience in leading technical projects or mentoring engineers.
- Communication & Business Acumen: Exceptional ability to distill complex technical topics (e.g., compute complexity, infrastructure costs) into clear, business-relevant insights for decision-makers.
- MLOps Proficiency: Proven experience in optimizing ML pipelines and infrastructure. Familiarity with tools like MLflow, Kubeflow, Airflow, and Data Versioning tools (DVC, etc.).
- Engineering First: Proficient in Python, Docker, and Kubernetes. You treat ML models as software artifacts that need testing and version control.
- NLP & LLM Expertise: Experience with Transformer architectures, Embeddings, and LLM fine-tuning. Familiarity with frameworks like PyTorch, Hugging Face, and vLLM.
- Language Support: Experience processing or fine-tuning models for multi-lingual environments.

## Nice to Have

- Multimodal Expertise: Experience working with Multimodal models (Image-to-Text, Text-to-Image, VLMs like CLIP, LLaVA).
- Security Awareness: Understanding of GenAI security threats (e.g., Prompt Injection).
- High-Performance Computing: Experience optimizing inference speed (quantization, distillation, vLLM) for real-time applications.
- Vector Database: Experience with Vector DBs for RAG applications.

Why Join Us?

- Innovative Environment: Be part of a company at the forefront of technology to provide security in GenAI, with opportunities to work on groundbreaking projects.
- Growth Opportunities: Take your career to new heights with our career development programs and growth-focused culture.
- Dynamic Team: Join a multi-cultural and dynamic team of dedicated professionals who inspire and support each other.
- Compensation: Competitive salary and benefits package, commensurate with experience and performance.

Application Process

- If you're ready to embark on this exciting journey and contribute to shaping the future of GenAI security, please submit your resume outlining your relevant experience and motivation for applying.
- If you prefer a direct connection or have specific questions about our vision, feel free to reach out to our Co-founder, Alvin Kwock, via [LinkedIn](https://www.linkedin.com/in/alvin-kwock-8324052/).

## About AIFT

## Company Overview
- **One-liner**: AIFT (Laboratory for AI-Powered Financial Technologies) is a Hong Kong-based research laboratory and innovation hub that develops AI and data science solutions to transform global financial services.
- **Entity Type**: Private (research lab with spin-off commercialization arm)
- **Headquarters**: Hong Kong Science Park, Shatin, Hong Kong
- **Founded**: Not publicly available
- **Founders**: Co-founded by City University of Hong Kong and Columbia University. Key leadership includes Prof. Houmin Yan (Director), Shi Lei (Deputy Head of Commercialization), and Antony Zhu (Head of Product & Development).

## Core Business
- **Primary industry**: Financial technology (FinTech), AI research & development, blockchain/tokenisation
- **Target customers**: B2B – banks, financial institutions, SMEs, and enterprise clients in the finance sector
- **Mission / purpose**: "Conducting pioneering research, attracting the brightest talents, realizing visionary concepts, and providing the best business solutions for future financial services." [hkaift.com](https://hkaift.com/careers/)

## Products & Services
- **AI-Driven Financial Service**: Research and solution development for AI-powered risk management, investment intelligence, and customer analytics.
- **AI-Enhanced Financial Technology**: Advanced analytics and machine learning models for credit scoring, fraud detection, and regulatory compliance.
- **Social Media Analytics for Financial Technology**: Platforms that mine social media and news data for market sentiment and investment signals.
- **FinSights (Supply Chain Finance)**: A solution showcased at InnoEX 2026 that leverages trusted logistics data to optimize trade finance. [hkaift.com](https://hkaift.com/)
- **Tokenised Deposit Platform**: A joint initiative with JETCO to enable commercial banks to issue and manage tokenised deposits. [hkaift.com](https://hkaift.com/)
- **Tolo Digitech (Spin-off)**: Partnered with fintech firm FUNDel to build a trade finance platform integrating Port Community System data. [hkaift.com](https://hkaift.com/)
- **Portfolio Management System for E-Commerce Loan Securitisation**: A system referenced in AIFT’s portfolio (see About page). [hkaift.com](https://hkaift.com/about-us/)

## Market Standing
- **Valuation / Market Cap**: Not disclosed
- **Key Metric**: Headcount of ~26 employees (as of 2025-2026), with a -17.4% yearly change reported on LinkedIn. [linkedin.com](https://hk.linkedin.com/company/laboratory-for-ai-powered-financial-technologies-limited)
- **Notable Investors / Partners**: JETCO (joint tokenisation platform), FUNDel (trade finance spin-off), Hong Kong Science and Technology Parks (HKSTP), City University of Hong Kong, Columbia University.
- **Growth Signals**: Spin-off company Tolo Digitech launched in 2026; participation in InnoEX 2026; active patent filings (e.g., entity association recommendation, cryptocurrency asset classification). However, headcount declined year-over-year, so growth trajectory appears mixed.

## Competitive Advantages
- **Academic pedigree**: Founded jointly by City University of Hong Kong and Columbia University, giving it deep research capabilities and access to top talent.
- **Ecosystem position**: Located in Hong Kong Science Park, a major R&D hub, and holds strong ties with local banks, regulators, and fintech players.
- **Full-stack FinTech approach**: Combines AI, blockchain, and data science to address both research and commercial deployment (spin-offs, partnerships).
- **Proven commercialization**: Multiple patents granted via Google Patents and live solutions (tokenised deposits, trade finance platform).

## Strategic Focus
- Commercializing research outcomes into market-ready FinTech products.
- Expanding tokenised deposit and digital asset infrastructure for banks.
- Scaling supply chain finance and trade finance solutions via data partnerships (e.g., Port Community System).
- Building a pipeline of spin-off companies to take innovations to market.
- Attracting talent for both research and product development roles (openings listed on [Greenhouse careers page](https://job-boards.greenhouse.io/aift) and internally).

## Why Work Here
- **Mission-driven**: Work at the intersection of AI and finance to solve real-world problems in banking, risk, and trade.
- **Young and growing**: Described as a "young company" with opportunities to get in "on the ground floor" and grow as projects gain momentum. [hkaift.com](https://hkaift.com/careers/)
- **International connections**: Collaborations with top universities and global financial institutions provide a broader perspective.
- **Practical skill development**: Talent training is part of the mission – developers and analysts can build expertise in FinTech ecosystems (Python, Spring Boot, Docker, blockchain, etc.). [hkaift.com](https://hkaift.com/careers/)
- **Vibrant community**: Active member of Hong Kong Science Park, Hong Kong’s largest R&D base, with access to a network of tech companies and events.
- **Work environment**: Office-based in Hong Kong Science Park (Shatin); roles include Software Engineer, Assistant Quantitative Analyst, Data Analyst, and Research Assistant. [hkaift.com](https://hkaift.com/careers/)
- **Culture**: Emphasizes open innovation, cross-boundary collaboration (universities, VCs, financial institutions), and a flat, research-driven atmosphere.

## Sources
1. [hkaift.com (Home Page)](https://hkaift.com/)
2. [hkaift.com (Careers Page)](https://hkaift.com/careers/)
3. [hkaift.com (About Us Page)](https://hkaift.com/about-us/)
4. [LinkedIn Company Profile](https://hk.linkedin.com/company/laboratory-for-ai-powered-financial-technologies-limited)
5. [HKSTP Directory Page](https://www.hkstp.org/en/directory/innohk/laboratory-for-ai-powered-financial-technologies-limited)
6. [Greenhouse Careers Page](https://job-boards.greenhouse.io/aift)

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