AI Software Engineer - Strategic Office - Toptier MNC (English Speaking)

Ignite Recruitment Hong Kong Limited; Hong Kong Island; Sur place; HKD 900 000 - 1 300 000; Plein temps

Résumé du poste

Ignite Recruitment Hong Kong Limited seeks an AI Software Engineer for the Strategic Office to build practical AI tools and production services using LLMs, data engineering, and strong software discipline. The role emphasizes turning high-level goals into working tools and scalable backend services.

The successful candidate will lead autonomous agent development, deliver near real-time executive insights, and ensure governance and security across platforms, reporting to the CEO Office.

Qualifications

• 6+ years of hands-on experience as a software, platform, AI, or data engineer.
• Experience deploying LLMs in real-world systems, with agent frameworks, RAG, vector stores, and tool orchestration.
• Strong Python programming skills for backend services, automated workflows, and data handling.
• Cloud & DevOps experience (AWS, Azure, or GCP) with CI/CD and core MLOps/LLMOps disciplines.
• C‑Suite visualization: ability to design clear, intuitive web interfaces and data visualizations for executives.

Responsabilités

• Build Autonomous Agents: design and deploy AI agents and automated data pipelines.
• Deliver Executive Insights: create low-latency dashboards and reporting tools for CEO decision-making.
• Scale Backend Services: architect and maintain robust backend infrastructure for high-volume AI workloads.
• Apply Practical ML: implement data analytics and forecasting models for enterprise decisions.
• Enforce Best Practices: uphold CI/CD, testing, version control, and documentation standards.
• Ensure Alignment: ensure data/AI solutions comply with group standards and security guardrails.
• CEO Innovation Lab: prototype tools for the CEO and mature experiments into production services.
• Manage LLM Lifecycles: operate MLOps/LLMOps pipelines with monitoring and rollback mechanisms.
• Navigate Governance: coordinate change through governance channels for ERP integrations.
• Security, Reliability & Scale: safeguard data and optimize performance across platforms.

Connaissances

Python

LLM Deployment

Agent Frameworks

Docker

Kubernetes

CI/CD

Data Pipelines

Formation

Bachelor’s degree in Computer Science

Outils

AWS

Azure

GCP

MLflow

Kubeflow

Vector Stores

Description du poste

AI Software Engineer - Strategic Office - Toptier MNC (English Speaking)

AstheAISolutionsEngineer,OfficeoftheGroupCEO,youwillbetheprimarytechnicalpartnerresponsibleforbuildingpracticalAItoolsandworkflowsthatreduceinformationlatencyandsupportfasterstrategicdecisions.

Thisisabuilderrolefocusedonturninghigh-levelgoalsintoworkingtools,prototypesandproduction-readyservicesusingLLMs,agenticAI,dataengineeringandstrongsoftwarediscipline.Itisnotanarchitecture-onlyorresearchrole;successismeasuredbyreliablesolutionsthatworkday-to-day.Yourdaywilltypicallyinclude:

Build Autonomous Agents: Design and deploy AI agents and automated data pipelines to extract, structure, and synthesize complex insights across internal corporate systems and external platforms.

Deliver Executive Insights: Construct low-latency dashboards, unified visual interfaces, and reporting tools that aggregate cross-functional data to directly support the CEO's decision-making.

Scale Backend Services: Architect and maintain robust backend infrastructure, platform components, and data pipelines optimized for high-volume AI workloads.

Apply Practical ML: Implement data analytics, forecasting models, and light machine learning where they materially elevate enterprise decision quality.

Enforce Best Practices: Drive rigorous software engineering standards, including CI/CD, automated testing, version control, infrastructure automation, and clear technical documentation.

Ensure Alignment: Guarantee all data and AI solutions fully comply with group technology standards, security frameworks, and architectural guardrails.

CEO Innovation Lab: Rapidly build and test bespoke prototype tools for the CEO, successfully maturing high-performing experiments into production-grade enterprise services.

Manage LLM Lifecycles: Operate MLOps/LLMOps pipelines for model packaging, version control, deployment, live monitoring, and rollback mechanisms.

Navigate Governance: Coordinate change management through proper governance channels for integrations involving PARIS (the in-house ERP), transitioning tools to operational ownership while retaining management over specific applications.

4. Security, Reliability & Scale

Secure by Design: Safeguard sensitive commercial and operational data by enforcing strict access control, data protection protocols, and environment segregation.

Operational Resilience: Collaborate with the central Technology, Cybersecurity, and Operations teams to define runbooks, support models, and incident response workflows.

Optimize Performance: Continuously enhance platform robustness, processing speed, cloud architecture, and cost efficiency.

Stakeholder Relations

Internal:

Office of the CEO : Align directly on strategic priorities, prototyping needs, and rapid product delivery.

Partner on corporate tech standards, security, architecture, and transitioning

Cross-Functional Stakeholders: Collaborate with business, finance, research, and operational teams to define problems, validate data, and drive tool adoption.

External:

Cloud & AI Vendors: Navigate platforms across AWS, Azure, and Google Cloud.

Specialist Data Providers: Integrate high-consequence external data feeds from Bloomberg, Clarksons, S&P Global, and other maritime or financial networks.

Job Experience & Qualifications

Experience: 6+ years of hands-on experience as a software, platform, AI, or data engineer, with a proven track record of shipping production-grade systems.

AI & LLM Expertise: Practical experience deploying LLMs in real-world systems, with deep familiarity in agent frameworks, RAG (Retrieval-Augmented Generation), vector stores, and tool orchestration.

Backend Mastery: Strong Python programming skills for backend services, automated workflows, and data handling. Proven experience building robust APIs and multi-source data pipelines.

Cloud & DevOps: Hands‑on cloud engineering experience (AWS, Azure, or GCP) deploying live services. Solid grounding in modern CI/CD, testing, version control, and core MLOps/LLMOps disciplines.

C‑Suite Visualization: Ability to design clear, intuitive web interfaces and data visualizations that translate complex metrics for senior executives.

Education: Bachelor’s degree in Computer Science, Data Engineering, Mathematics, or a highly technical STEM discipline.

Proficiency with dedicated MLOps/LLMOps tooling (e.g., MLflow, Kubeflow, model registries, evaluation pipelines).

Experience with containerization and orchestration workflows (Docker, Kubernetes).

Prior experience building tools tailored for executive leadership, trading desks, research analysts, or high-consequence decision environments.

Exposure to highly regulated, operationally critical environments or specialized maritime, logistics, and commodities market data.