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Associate Director, Software Engineering
- Compensation
- Not disclosed
- Location
- Not listed
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 10+ years
- Category
- Software engineering
- Posted
Skills
Must have
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related discipline.
- 10+ years of experience in relevant technologies, with strong hands-on delivery experience across AI, ML, data, and enterprise technology solutions.
- Strong stakeholder management skills.
- Deep knowledge of LLM and agent-based solution design, including prompt engineering, Agentic RAG, knowledge retrieval, memory design, and orchestration patterns.
- Experience building or improving knowledge management and knowledge base solutions for structured and unstructured content, including document parsing, indexing, retrieval, and search optimisation.
- Good understanding of machine learning and deep learning techniques, such as model training, fine-tuning, multimodal AI, computer vision, NLP, or speech-related applications.
- Experience working with modern AI and engineering ecosystems, such as open-source AI frameworks, vector databases, APIs, and cloud platforms including AWS, Azure, or GCP.
- Solid software engineering discipline, including testing, CI/CD, performance optimisation, observability, and production support considerations.
- Strong verbal and written English communication skills, with the ability to work effectively across multiple sites, cross-functional teams, and different business and technology departments.
- Proven ability to collaborate with stakeholders across regions and functions, align diverse requirements, and drive delivery in a complex enterprise environment.
Full description
Some careers have more impact than others.
If you’re looking for a career where you can make a real impression, join HSBC and discover how valued you’ll be.
We are currently seeking an experienced professional to join our team in the role of Associate Director, Software Engineering.
Business: IWPB Technology
Principal responsibilities
- Lead the evaluation and adoption of AI and ML technologies for CEAIA, with a focus on LLM, agentic AI, RAG, knowledge management, and emerging engineering patterns.
- Drive technical research, proof-of-concept development, and prototype delivery to validate new ideas and accelerate platform evolution.
- Accountable for the end-to-end delivery of the service, ensure the solution is aligned with group direction, NFR and quality expectations are met.
- Partner with development teams to refine solution details, unblock technical issues, and support high-quality implementation across the delivery lifecycle.
- Contribute to the design and improvement of knowledge base, retrieval, memory, orchestration, and search capabilities that enhance CEAIA platform effectiveness.
- Establish strong engineering practices across evaluation, testing, CI/CD, observability, and operational readiness for AI-enabled solutions.
- Work with stakeholders across business, product, architecture, and technology teams to identify opportunities, assess feasibility, and prioritise high-value innovation areas.
- Collaborate across multiple sites and different departments to drive alignment, delivery progress, and technical consistency.
- Provide technical leadership and practical guidance to engineers, helping raise the standard of AI engineering across the team.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related discipline.
- 10+ years of experience in relevant technologies, with strong hands-on delivery experience across AI, ML, data, and enterprise technology solutions.
- Strong stakeholder management skills.
- Deep knowledge of LLM and agent-based solution design, including prompt engineering, Agentic RAG, knowledge retrieval, memory design, and orchestration patterns.
- Experience building or improving knowledge management and knowledge base solutions for structured and unstructured content, including document parsing, indexing, retrieval, and search optimisation.
- Good understanding of machine learning and deep learning techniques, such as model training, fine-tuning, multimodal AI, computer vision, NLP, or speech-related applications.