WHO WE ARE:

As Singapore’s longest established bank, we have been dedicated to enabling individuals and businesses to achieve their aspirations since 1932. How? By taking the time to truly understand people. From there, we provide support, services, solutions, and career paths that meet their individual needs and desires.

Today, we’re on a journey of transformation. Leveraging technology and creativity to become a future-ready learning organisation. But for all that change, our strategic ambition is consistently clear and bold, which is to be Asia’s leading financial services partner for a sustainable future.

We invite you to build the bank of the future. Innovate the way we deliver financial services. Work in friendly, supportive teams. Build lasting value in your community. Help people grow their assets, business, and investments. Take your learning as far as you can. Or simply enjoy a vibrant, future-ready career.

Your Opportunity Starts Here.

AI Platform Engineer (AVP) – Enterprise AI Platform

About the Role

We are seeking an experienced AI Platform Engineer (AVP) to design, build, and operate OCBC’s enterprise AI platform. This role focuses on delivering secure, scalable, and production-grade AI capabilities that enable teams to develop, deploy, and manage AI/ML and GenAI workloads across the organisation.

You will work at the intersection of platform engineering, MLOps, and enterprise AI governance , supporting use cases across engineering, operations, and business domains. The role requires strong hands-on engineering capability combined with the ability to operate within a regulated financial services environment (MAS TRM / outsourcing guidelines) .

Why Join OCBC AI Platform Team

1. AI Platform Engineering & Architecture

• Design and implement scalable AI platform capabilities supporting LLMs, RAG pipelines, and model serving

• Build GPU-aware, autoscaling inference platforms (Kubernetes-based) for low latency, production-grade workloads

• Develop reusable platform services (APIs, SDKs, gateways) to enable enterprise-wide AI adoption

• Implement multi-tenant platform controls including quota management, cost optimisation, and resource isolation

2. Model Serving, MLOps & Automation

• Establish end-to-end ML lifecycle capabilities covering training, evaluation, deployment, and monitoring

• Implement CI/CD and GitOps pipelines for model promotion and environment provisioning

• Integrate ML tooling (e.g. MLflow, Ray) for experiment tracking and orchestration

• Design evaluation frameworks to assess model quality, accuracy, and hallucination risks

3. Security, Risk & Compliance

• Implement platform security controls including network segmentation, mTLS, policy enforcement, and secrets management

• Ensure AI solutions comply with enterprise security standards and MAS regulatory requirements

• Embed responsible AI guardrails and governance controls in collaboration with cybersecurity and risk teams

4. Platform Operations & Reliability

• Build observability capabilities (metrics, logging, tracing) using tools such as Prometheus, Grafana, OpenTelemetry

• Monitor and optimise platform performance, scalability, and cost efficiency

• Proactively identify risks, dependencies, and bottlenecks, and implement mitigation strategies

5. Developer Enablement & Adoption

• Provide self-service tooling, documentation, and reference architectures for engineering teams

• Guide teams on best practices in MLOps, platform usage, and cost optimisation (FinOps)

• Translate platform capabilities into clear business value to drive adoption

6. Collaboration & Delivery

• Partner with product, engineering, and infrastructure teams to deliver shared platform capabilities

• Break down complex requirements into clear execution plans and milestones

• Support build vs buy decisions for AI technologies and solution

Requirements

Technical Skills

• Strong hands-on experience building cloud-native platforms (AWS preferred) and Kubernetes-based systems

• Proficiency in Python and experience building scalable backend or platform services

• Experience with AI/ML systems , including LLMs, RAG, fine-tuning, and model serving

• Knowledge of containerisation, service mesh, networking, and distributed systems

• Familiarity with CI/CD, GitOps, and infrastructure automatio

Domain & Platform Experience

• Experience delivering shared platforms or enterprise services at scale

• Strong understanding of AI/ML lifecycle and production operations

• Experience designing APIs and cloud-based microservices architectures

Soft Skills & Leadership

• Strong problem-solving skills with ability to operate in complex and ambiguous environments

• Effective communication and stakeholder management across teams

• Ability to mentor junior engineers and contribute to team capability building

Nice to Have

• Experience with LLM serving frameworks (e.g. vLLM, Triton)

• Exposure to developer platforms or productivity engineering initiatives

• Understanding of enterprise AI governance, model risk, and compliance frameworks

• Background as a full-stack developer , with ability to build end-to-end applications to support platform adoption

Role Positioning (AVP Level)

• Hands-on engineering role with ownership of key platform components

• Provides technical leadership without full people management responsibility

• Acts as a bridge between architecture, engineering, and operations teams

• Contributes to platform standards, guardrails, and engineering best practices

What we offer:

Competitive base salary. A suite of holistic, flexible benefits to suit every lifestyle. Community initiatives. Industry-leading learning and professional development opportunities. Your wellbeing, growth and aspirations are every bit as cared for as the needs of our customers.