Head of Data / AI (J-2629)

Quam Plus Financial; Hong Kong; Sur place; HKD 600 000 - 900 000; Plein temps

Résumé du poste

Quam Plus Financial in Hong Kong is seeking an experienced AI and Data Platform Lead to design, build, and scale robust AI/ML platforms and infrastructure across on‑premises and hybrid environments. You will drive MLOps, LLMOps, and AgentOps to accelerate model development and lifecycle management.

You will deploy and optimize LLM and agent workflows with emphasis on performance, cost, reliability, and observability; develop ETL/ELT pipelines; architect a modern data warehouse / Lakehouse;

Responsabilités

• Lead the design, development, and scaling of robust AI/ML platforms and infrastructure (on-premises and hybrid).
• Drive MLOps, LLMOps, and AgentOps practices to accelerate model development, deployment, and lifecycle management.
• Deploy, optimize, and scale Large Language Model (LLM) and agent workflows with a focus on performance, cost, reliability, and observability.
• Develop, optimise, and maintain robust ETL/ELT pipelines for data ingestion, transformation, and loading from internal systems, external feeds, and market data sources.
• Architect and implement a modern data warehouse / Lakehouse, including data modelling standards, dimensional modelling, and performance optimisation.
• Collaborate with AI and business teams to deliver clean, feature-ready data for models, RAG systems, and decision-making use cases.
• Fine-tune models, design robust evaluation and monitoring systems, and implement multi-agent architectures with appropriate guardrails.
• Evaluate emerging technologies, lead proof-of-concept initiatives, and implement Infrastructure-as-Code (Kubernetes, etc.).
• Build, mentor, and lead a high-performing AI solution team.
• Champion the adoption of AI-assisted development practices across the organisation.
• Serve as the key bridge between business stakeholders and technical teams, translating requirements into deliverable AI initiatives.
• Actively contribute hands-on through architecture reviews, solution design, and critical implementation work.
• Ensure high availability, performance, and cost-efficiency of AI infrastructure.
• Maintain secure, compliant, and repeatable deployment processes in line with regulatory standards.
• Manage operational excellence, incident response, and continuous improvement of AI platforms.

Description du poste

• Lead the design, development, and scaling of robust AI/ML platforms and infrastructure (on-premises and hybrid).
• Drive MLOps, LLMOps, and AgentOps practices to accelerate model development, deployment, and lifecycle management.
• Deploy, optimize, and scale Large Language Model (LLM) and agent workflows with a focus on performance, cost, reliability, and observability.
• Develop, optimise, and maintain robust ETL/ELT pipelines for data ingestion, transformation, and loading from internal systems, external feeds, and market data sources.
• Architect and implement a modern data warehouse / Lakehouse, including data modelling standards, dimensional modelling, and performance optimisation.
• Collaborate with AI and business teams to deliver clean, feature-ready data for models, RAG systems, and decision-making use cases.
• Fine-tune models, design robust evaluation and monitoring systems, and implement multi-agent architectures with appropriate guardrails.
• Evaluate emerging technologies, lead proof-of-concept initiatives, and implement Infrastructure-as-Code (Kubernetes, etc.).

Team Leadership & Business Alignment

• Build, mentor, and lead a high-performing AI solution team.
• Champion the adoption of AI-assisted development practices across the organisation.
• Serve as the key bridge between business stakeholders and technical teams, translating requirements into deliverable AI initiatives.
• Actively contribute hands-on through architecture reviews, solution design, and critical implementation work.

Infrastructure, Operations & Governance

• Ensure high availability, performance, and cost-efficiency of AI infrastructure.
• Maintain secure, compliant, and repeatable deployment processes in line with regulatory standards.
• Manage operational excellence, incident response, and continuous improvement of AI platforms.