VP/AVP, Tech Lead, Enterprise GenAI Platform, Data Platform, Group Technology

Job Purpose

The Data Platform team owns the bank's enterprise Generative AI capabilities and the cloud data infrastructure that supports business units across the bank. We are looking for a Tech Lead to take ownership of the architecture, security posture and cost efficiency of our GenAI platform on Google Cloud. The role combines hands-on AI engineering with cloud architecture, security and networking design, cloud financial management and technology risk governance, and provides senior technical leadership across the platform's data engineering estate.

Key Responsibilities

• Lead the architecture, delivery and operation of enterprise GenAI services on Google Cloud, including
  knowledge search, retrieval-augmented generation and conversational assistants.
• Design agent and orchestration patterns for LLM-based applications that comply with bank policy on
  autonomous systems and enforce appropriate access controls on retrieved data.
• Establish secure execution patterns for tool-enabled AI workflows so that generated outputs and actions
  remain within enterprise isolation boundaries.
• Assess third-party and open-source AI products for deployment in isolated, tightly controlled
  environments, and work with vendors on the architectural changes needed to meet bank standards.
• Design private, zero-trust connectivity for AI and data services on GCP, covering private endpoint
  access, VPC and subnet design, DNS-based traffic steering and regional endpoint strategy.
• Own identity, access and role-based control models for AI workloads, model endpoints and data
  access.
• Produce technical risk assessments and layered security designs, and take solutions through
  Information Security, Technology Risk and Architecture governance.
• Forecast LLM consumption and inference demand across model tiers to inform capacity commitments
  and pricing model selection.
• Reduce inference cost and latency through caching strategies, model selection and workload
  right-sizing.
• Lead annual cloud capacity and budget planning for the platform and drive elimination of cloud waste.
  Prepare cost-of-ownership analyses and business cases for on-premise to cloud migrations for senior
  management.
• Provide technical leadership over large-scale batch and distributed data pipelines and their migration to
  managed cloud services, including resolution of production performance issues.
• Establish platform observability, alerting and reliability targets.
• Mentor engineers, review designs and set engineering standards for AI and data workloads.
• Represent the platform in discussions with Information Security, cloud governance functions, vendors
  and business stakeholders.
• Participate actively in Agile delivery and contribute to engineering excellence across the organisation.

Job Requirements

• Master's degree in Artificial Intelligence, Machine Learning, Data Science or a closely related discipline;
  Bachelor's degree in Computer Science, Information Technology or a related discipline.
• Minimum 10 years of technology experience, including at least 3 years within the banking or financial
  services industry.
• Google Cloud Certified Professional Cloud Architect (active credential required).
• Hands-on experience designing and operating production GenAI or LLM-based platforms on Google
  Cloud for enterprise users.
• Strong GCP security and networking expertise, including private connectivity to managed services, VPC
  and subnet design, DNS routing and multi-region architectures, together with IAM and role-based
  access design.
• Solid understanding of LLM cost and capacity management: consumption modelling, reserved versus
  on-demand capacity trade-offs, caching approaches and inference cost optimisation.
• Experience delivering AI solutions in isolated or highly restricted environments and securing Information
  Security and Technology Risk approvals for them.
• Experience with agent orchestration frameworks and tool-enabled LLM workflows in a regulated
  enterprise setting.
• Strong data engineering background with distributed processing frameworks such as Apache Spark,
  including production troubleshooting and performance tuning, and proficiency in SQL.
• Strong programming skills in Python; working knowledge of Java.
• Experience with CI/CD tooling, containerisation and modern observability stacks.
• Strong analytical, problem-solving and communication skills, with the ability to engage security, risk,
  vendor and business stakeholders.
• Ability to work proactively and independently, and to operate with ambiguity in an evolving GenAI
  governance landscape.
• Experience building conversational AI or virtual assistants in an enterprise setting is highly desirable.
• Experience with on-premise to cloud migration of big data platforms and associated cost analysis is
  highly desirable.
• Familiarity with machine learning frameworks and search or vector retrieval technologies is a plus

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