Machine Learning Engineer

IO TECH SOLUTIONS LIMITED; Hong Kong; On-site; HKD 600,000 - 1,200,000; Full time

Job summary

IO TECH SOLUTIONS LIMITED is seeking a highly skilled Machine Learning Engineer to join a dynamic project team focused on transforming data-driven supervision through cutting-edge AI. You will architect, develop, and deploy GenAI and ML solutions, integrate LLMs into enterprise workflows, and manage the full AI lifecycle in a highly regulated financial environment.

The role demands deep Python expertise, MLOps, LLMOps, data governance, and strong CI/CD, with Agile delivery and collaboration

Qualifications

• Extensive experience with AI tech stack and Python development.
• Experience in MLOps/LLMOps including model governance and monitoring.
• Strong CI/CD experience for ML models and production pipelines.
• Understanding of data governance, security, and regulatory requirements.
• Familiarity with Agile methodologies and cross-functional collaboration.

Responsibilities

• Architect, develop, and deploy production-quality GenAI and machine learning solutions.
• Integrate Large Language Models (LLMs) into enterprise workflows and supervisory systems.
• Manage the end-to-end AI lifecycle: data ingestion, model training, fine-tuning, and monitoring.
• Collaborate across multi-disciplinary teams to bridge experimentation and production-grade apps.
• Ensure AI systems meet security, data governance, and regulatory compliance.

Skills

Python

MLOps

LLMOps

CI/CD

Agile

Data governance

Job description

My client, a prominent financial institution provider, is seeking a highly skilled Machine Learning Engineer to join a dynamic project team focused on transforming data-driven supervision through cutting-edge artificial intelligence.

What you'll do

• Architect, develop, and deploy production-quality GenAI and machine learning solutions.
• Integrate Large Language Models (LLMs) into existing enterprise workflows and supervisory systems.
• Manage the end-to-end AI lifecycle, including data ingestion, model training, fine-tuning, and performance monitoring.
• Collaborate across multi-disciplinary teams to bridge the gap between experimental modeling and production-grade applications.
• Ensure AI systems meet rigorous standards for security, data governance, and regulatory compliance.

The experience and skills needed

• Extensive experience in the full AI technology stack, with deep proficiency in Python.
• Demonstrated success in MLOps and LLMOps, including model governance and real-time monitoring.
• Practical experience building scalable AI applications within a secure or highly regulated environment.
• Strong understanding of Agile methodologies and continuous integration/continuous deployment (CI/CD) pipelines.
• Excellent analytical skills with the ability to turn complex concepts into reliable, sustainable, and performant solutions.