Sr. Associate Director, Software Engineering(Group AI Platform Architecture)

Define and govern architecture for the AI Platform: reference architectures, standards, guardrails, patterns and roadmaps across cloud and also on‑prem environments. Provide technical direction and design authority for engineering teams, including solution reviews, design approvals, and delivery oversight. Lead vendor/product evaluation and adoption (e.g. GPU platforms, Kubernetes distributions, storage and networking solutions, observability stacks), ensuring fit with HSBC infrastructure requirements, support models, and lifecycle management. Partner closely with other HSBC platform teams on on‑prem platform development, integration with existing data center capabilities, and operational readiness. Design and guide implementation of infrastructure for AI/ML workloads (capacity planning, scheduling, tenancy/isolation, performance, reliability, cost controls). Embed security, compliance, and operational controls by design, working with Cyber Security, Data, Model Risk Management (MRM), and other stakeholders (e.g., identity, encryption, logging, vulnerability management, auditability). Ensure production-grade non-functionals: availability, disaster recovery, monitoring/alerting, SLOs, incident readiness, and automation (IaC, CI/CD enablement where relevant). Proven architecture leadership for large-scale platforms, including defining reference architectures, standards, guardrails, and roadmaps across cloud and on‑prem. Proven ability to produce clear, practical designs that translate business and technical requirements into secure, scalable, reusable and maintainable platform solutions. Hands-on experience building and delivering platform capabilities across both cloud and on-premises environments, with a strong understanding of production engineering and operational considerations. Good knowledge of one or more major cloud platforms such as AWS, GCP, Azure, or Alibaba Cloud, including core services, data, networking, identity, and security principles. Experience with modern platform engineering practices, including APIs, containerisation, Kubernetes, CI/CD, automation, and observability. Familiarity with infrastructure as code, policy as code, and reusable platform patterns. Experience evaluating and adopting vendor products/services (e.g., Kubernetes distributions, GPU solutions, observability tooling) and aligning them to enterprise support, lifecycle, and operational models. Strong stakeholder management skills: able to build trusted relationships with engineering, product, and control functions; translate complex requirements into pragmatic platform designs.