DBS Bank Hiring! Full Time VP-AVP, Tech Lead, Enterprise GenAI Platform, Data Platform, Group Technology in East Region (Singapore) - Ricebowl

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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