Job Description Data & AI Delivery Lead
Role Overview As the technical delivery lead, you will take full ownership of the technical solutions and delivery of Data & AI projects. You will translate business strategy and product plans into executable technical solutions, leading the team to deliver high-quality and high-efficiency outcomes. Your core value lies in using technology to drive business results - acting as the key "engine" that turns ideas into reality within the team.
I. Core Responsibilities
1. Project Delivery & Technical Implementation
- Based on business priorities and strategic objectives, formulate technical roadmaps, solutions, project milestones, and resource plans. Take full accountability for delivery timelines, quality, and cost.
- Lead technology selection, system architecture design, and resolution of critical technical challenges to ensure optimal performance, scalability, stability, and cost efficiency.
- Establish and continuously optimise delivery processes (including Agile development, CI/CD, MLOps, etc.) to improve team delivery efficiency and production environment stability.
2. Technology Market Tracking & Technology Partner Evaluation
- Continuously monitor AI technology trends in China and globally, maintaining sharp insight into cutting-edge technologies (e.g., large language models, AI agents, multimodal AI).
- Conduct technology scouting, proactively identify and screen potential technology partners in the market, and evaluate the suitability of their solutions for internal implementation.
- Ensure introduced technologies are highly aligned with the company's strategic business goals and drive technical evaluation reports and pilot validations.
3. Technical Team Management & Development
- Manage the day-to-day operations of the technical team, including task allocation, progress tracking, performance evaluation, talent development, and team structure building to ensure team capabilities match business growth.
- Foster a team culture of "engineering excellence, results-oriented mindset, and open collaboration" while promoting technical standards and knowledge sharing.
4. Cross-Functional Collaboration & Communication
- Work closely with product, operations, and business teams to accurately understand business requirements and translate them into executable technical tasks.
- Provide regular project updates, risk reports, and resource requirements to ensure transparency and offer strong technical support for decision-making.
5. Technical Architecture & Standards Execution
- Under the overall technical architecture and data governance framework, ensure the implementation of specific projects complies with architectural standards and security/compliance requirements.
- Continuously track frontier technologies in the Data & AI domain, evaluate new tools and methods that can improve efficiency or product quality, and propose adoption and optimisation recommendations.
II. Requirements
(1) Mandatory Requirements
- Bachelor's degree or above in Computer Science, Software Engineering, Mathematics, Statistics, Artificial Intelligence, or related fields.
- 5+ years of experience in Data & AI, including at least 2 years of technical team management experience.
- Led at least 3 complex Data or AI projects from 0 to 1, with proven experience in large-scale operations.
- Technical stack requirements:
- Strong knowledge in data domain: data warehouses, data lakes, lakehouse architecture; familiar with mainstream cloud-native data services (AWS/Azure/GCP) or open-source technologies (Spark, Flink, Hudi, Iceberg).
- Strong knowledge in AI domain: machine learning/deep learning frameworks (TensorFlow/PyTorch); deep understanding of model training, inference, and deployment; familiar with RAG, AI agents, and large model application development patterns, with practical MLOps experience.
- Proficient in at least one programming language (Python/Scala/Java), with strong system design and code refactoring skills.
- Familiar with microservices, containerisation, DevOps, and other engineering practices.
- Proficient in SQL and familiar with NoSQL databases, with hands-on experience in performance tuning.
(2) Soft Skills
- High sense of responsibility and strong execution ability: results-oriented, able to break down strategy into actionable plans and lead the team to overcome challenges to achieve goals.
- Excellent collaboration and communication skills: good listener who can clearly and efficiently communicate project status and technical solutions with superiors and business stakeholders.
III. Preferred Qualifications
- Experience in delivering Data & AI projects in finance, insurance, or other highly regulated industries (e.g., healthcare, government).
- Experience in data analytics, data governance, data security, and compliance frameworks.
- Experience building AI platforms or products from 0 to 1.
- Experience in technology ecosystem partnerships, technical partner evaluation, or technology introduction and implementation.
- Strong understanding of domestic and international AI technology landscapes with good technical foresight.
- Proficiency in Cantonese.
