Associate Director, Data and Analytics

Role purpose (overall high-level summary of the role)

Group AI Management and Strategy (AIMS) team is responsible for developing and implementing Group-wide strategic programs across HSBC aimed at accelerating the commercialisation and delivery of Artificial Intelligence / Machine Learning (AI/ML) across HSBC. Key areas of focus include responsible development practices, common platforms and capabilities, AI technology enablement, governance, and the Group-wide AI Strategy.

We are seeking a hands-on and detail-oriented Lead AI Engineer, to join our Group AI Management and Strategy (AIMS) team. In this role, you will be responsible for designing, developing, deploying and maintaining robust Machine Learning and Generative AI solutions within HSBC. You will partner with value streams, businesses, and functions to deliver scalable, production-grade AI systems that drive business value. The ideal candidate will have a strong engineering mindset, excellent communication skills, and a passion for driving innovation through AI technology.
Key Responsibilities:

AI/ML Solution Architecture:

- Design end-to-end machine learning and AI systems that solve group-wide strategic problems; ensure architecture support scalability, real-time inference, and model governance

Model Development & Experimentation:

- Develop, train, and validate machine learning models using advanced techniques (deep learning, ensemble methods, transfer learning, LLMs, agentic systems); conduct rigorous A/B testing and statistical validation

Production AI/ML Systems:

- Build production-grade AI-ML pipelines including model training, evaluation, deployment, monitoring, and retraining workflows; implement AIOps,MLOps for models & LLMs; ensure model reproducibility and versioning

AI Guardrails & Safety:

- Implement guardrails, safety frameworks, and compliance controls for AI/ML systems; ensure models meet regulatory requirements, fairness standards, explainability requirements, and business risk tolerances

Responsible AI adoption:

- Collaborate with stakeholders to ensure responsible AI practices are adopted at scale, with a clear definition of metrics and benchmark for the same
Support for AI Use Cases:
- Provide guidance and support to value streams, businesses, and functions in the development of their own AI use cases, ensuring alignment with the Group-wide AI Strategy.
- Drive adoption of AI/ML solutions across business units; translate business problems into AI/ML opportunities; work with product teams to package AI/ML capabilities into scalable products and services

Model Monitoring & Governance:

- Implement monitoring frameworks for model performance drift, data drift, and business metrics; establish governance processes for model versioning, approval, and retirement
Stakeholder Engagement:
- Build and maintain strong relationships with stakeholders across the organization, including business leaders, technical teams, and compliance functions.
- Communicate effectively with stakeholders to gather feedback, provide updates, and address any inquiries related to AI products and use cases.
Documentation and Reporting:
- Prepare and maintain comprehensive documentation for AI products, including user guides, technical specifications, and project plans.
- Develop regular reports on the status of AI product development and deployment, highlighting key milestones, challenges, and opportunities.
Continuous Improvement:
- Stay informed about industry trends and best practices in AI product development and management to enhance the effectiveness of HSBC’s AI initiatives.

- Implement/assist cross-functional teams to develop MLOps framework and techniques
- Contribute to the continuous improvement of processes and methodologies related to AI product development.

Minimum Experience and Key Competencies:

10+ years of professional experience in end-end AI/ML engineering development, AI/ML architecture design, or a related role

Proven track record delivering 3+ ML/AI solutions to production preferably in enterprise or financial services environments

Experience in deploying Gen AI solutions/LLMs through integration with software applications – Open AI GPT models, Google Gemini, Mistral, etc. and orchestration frameworks like LangChain, LangGraph

Strong Python proficiency; clean code practices; expertise in designing modular, testable, maintainable ML systems; experience with design patterns and architectural principles. Hands-on experience in building new APIs in enterprise setting using modern, high-performance frameworks like FastAPI

Hands-on expertise in building & deploying solutions on the public cloud platforms AWS/GCP/Azure etc.; multi-cloud capability a plus.

Proven experience in deploying AI solutions at scale, in production, with experience working with big data

Ability to translate business problems into AI-ML opportunities; understand ROI, business metrics, and stakeholder requirements; drive adoption and commercialization.

Functional Knowledge

• Hands-on experience with large language models, prompt engineering, fine-tuning, retrieval-augmented generation (RAG), agentic systems, and considerations for production LLM deployments
• Understanding of model lifecycle management, version control, reproducibility, approval workflows, monitoring dashboards, and retirement processes
• Experience with low-latency inference, feature serving, online prediction systems, and streaming data processing for ML systems
• Understanding of embeddings, vector databases, semantic search, applications in RAG and Agentic AI systems
• Knowledge of model security, adversarial attacks, differential privacy, encryption, secure model serving, and compliance with data privacy regulations
• Knowledge of bias detection methods, fairness metrics, explainable AI techniques, and regulatory requirements around algorithmic transparency and discrimination

Others

• Highly competent, collaborative, and outcome-oriented, with a strong ability to drive results and navigate complex challenges.
• Tenacious and committed to delivering necessary outcomes in alignment with HSBC's mission and purpose.
• A strong focus on customer and teammate needs, with a dedication to serving and supporting them in achieving their goals.

SECTION B: Role Attributes

Role Attributes

Experience

Broad and comprehensive understanding of concepts and principles within multiple disciplines or a fast changing discipline

Decision-Making Authority

Owns technical decisions on model architecture, algorithms, and ML platform choices; collaborates with solution architects/leaders

Business / Commercial knowledge

The role requires a comprehensive understanding of several value units / products / regions / legal entities, alongside an advanced understanding of the industry and the relative positioning of the HSBC's products and services.

Problem Solving

Covers a wide range of activities that require complex judgements based on advanced analytical thought. Problems often require an innovative approach, based on extensive research

Innovation

Is proactive in developing ideas, continuously searching for improvements in techniques which add value to the business and has full responsibility for implementation.

 Complexity      Management

Handles inherent uncertainty in AI-ML; manages multiple concurrent projects at different stages; balances exploration with production stability

Accountability / decision making

Latitude to make decisions within the objectives of HSBC global business / global function overall. Little guidance is available and role holder is expected to formulate strategy following the objectives of the business.

Communication

The role requires an extremely high level of interactive listening, in order to negotiate with others often at the highest level.