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Singapore, Singapore
Onsite
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.
Why Join
Imagine being part of a team that harnesses the power of AI to drive business growth and innovation at OCBC. As an MLOps Engineering Lead, you’ll be pivotal in scaling our AI capabilities, bridging the gap between experimental models and robust, production-ready services that power our financial products. You’ll guide the development and maintenance of end‑to‑end ML pipelines, champion automation in deployments, and ensure models run securely, efficiently, and with high availability. You will play a crucial role in mentoring other MLOps engineers, fostering best practices, and collaborating cross-functionally to deliver impactful solutions to our customers and the business.
How you succeed
To succeed in this role, you'll be a technical leader at the forefront of MLOps advancements and cloud technologies, driving the design and implementation of highly scalable and reliable AI systems. You’ll collaborate with stakeholders to understand business requirements, translate them into technical roadmaps, and guide the team in delivering world-class AI solutions. This requires a blend of deep technical expertise, strategic thinking, and the ability to effectively communicate with both technical and non-technical audiences. You will be a champion for MLOps best practices and foster a culture of continuous improvement.
What you do
Lead the design, build, and maintenance of end‑to‑end MLOps and LLMOps pipelines.
Implement CI/CD workflows using tools such as Bitbucket, Jenkins, and other automation tools for automated testing, containerization, and release of AI models.
Containerize ML workloads with Docker and orchestrate them on Kubernetes (EKS) or other container platforms.
Architect and implement solutions leveraging AWS services (SageMaker, ECR, EKS/ECS, Lambda, S3, CloudWatch, CloudFormation, Terraform, etc.) to host, scale, and manage model training and inference pipelines.
Develop comprehensive monitoring and alerting solutions for model latency, accuracy, data drift, and infrastructure health; integrate with Prometheus, Grafana, CloudWatch, or similar tools.
Oversee the automation of model versioning, artifact storage, and metadata tracking using Mlflow or SageMaker model registry.
Collaborate closely with data scientists to package models as reproducible, production‑ready services (REST/gRPC APIs, batch inference jobs, streaming inference).
Ensure security, compliance, and governance of ML pipelines—manage IAM roles, encryption, audit logs, and data privacy controls.
Develop and document standards, best practices, and runbooks to enable smooth hand‑offs and knowledge sharing across teams, and mentor junior engineers.
Drive technical roadmaps and contribute to architectural decisions related to MLOps infrastructure.
Stay up to date with the latest MLOps trends and technologies and evaluate their potential application within OCBC.
Who you are
A degree in Computer Science, Software Engineering, Data Engineering, or a related field, with a strong foundation in both software development and machine‑learning concepts.
5+ years of experience in MLOps, DevOps, or cloud‑native engineering, preferably in a financial services or enterprise environment, with at least 2+ years in a leadership role.
Proficiency in Python (for scripting, SDK usage, automation) and model inferencing stack (Ray, VLLM, SGLang).
Hands-on experience with containerization (Docker) and orchestration (Kubernetes/EKS).
Expert knowledge of AWS cloud services (SageMaker, ECR/ECS/EKS, Lambda, Step Functions, S3, CloudWatch, IAM, CloudFormation/Terraform).
Strong understanding of CI/CD tools and infrastructure‑as‑code principles.
Extensive experience with ML lifecycle tools such as MLflow, Kubeflow, or SageMaker Pipelines.
Proven ability to solve complex problems and balance technical depth with business impact.
Excellent communication, collaboration, and mentorship abilities; comfortable working with data scientists, product owners, and operations teams.
Demonstrated experience leading technical projects and mentoring junior engineers.
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.
