Machine Learning Engineer

ioTech Solutions; Hong Kong; On-site; HKD 1,200,000 - 2,000,000; Full time

Job summary

ioTech Solutions is seeking a skilled Machine Learning Engineer to join a dynamic project team focused on transforming data-driven supervision through cutting-edge AI. You will architect, deploy, and productionize GenAI and ML solutions, integrating LLMs into enterprise workflows and supervisory systems.

You will manage the AI lifecycle from data ingestion to monitoring, collaborating with cross-functional teams to bridge research and production.

Qualifications

• Experience with full AI tech stack and production-grade ML systems.
• Proven ability to deploy GenAI and LLM-based solutions in production.
• Experience with secure or regulated environments.
• Familiarity with data ingestion, model training, and monitoring.
• Knowledge of Agile, CI/CD practices.

Responsibilities

• Architect, develop, and deploy GenAI and ML solutions.
• Integrate LLMs into enterprise workflows and supervisory systems.
• Manage AI lifecycle from data ingestion to monitoring.
• Collaborate with cross-functional teams to productionize models.
• Ensure AI systems meet security, governance, and regulatory standards.

Skills

Python

MLOps

LLMOps

Agile methodologies

CI/CD

Analytical skills

Regulatory compliance understanding

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.