Machine Learning Ops Engineer at OCBC in Singapore, Singapore

Machine Learning Ops Engineer Location: OCBC Singapore 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. Why Join Imagine being part of a team that harnesses the power of AI to drive business growth and innovation at OCBC. As a Machine Learning Ops (MLOps) Engineer, you’ll be the bridge that turns experimental models into production‑ready services that power our financial products. You’ll work on end‑to‑end ML pipelines, automate deployments, and ensure that models run securely, efficiently, and with high availability, all while collaborating with cross‑functional teams and seeing the direct impact of your work on our customers and the business. How you succeed To succeed in this role, you'll need to stay at the forefront of MLOps advancements and cloud technologies, applying them to deliver robust, production grade AI systems. This means collaborating with stakeholders to understand business needs, designing, and developing scalable AI solutions, and continuously monitor and improve model performance. You'll need to balance technical complexity with business acumen and communicate effectively with both technical and non-technical stakeholders. What you do Design, build, and maintain end‑to‑end MLOps and LLMOps pipelines Implement CI/CD workflows using tools such as Bitbucket, Jenkins for automated testing, containerization, and release of AI models. Containerize ML workloads with Docker and orchestrate them on Kubernetes (EKS) or other container platforms. Leverage AWS services (SageMaker, ECR, EKS/ECS, Lambda, Step Functions, S3, CloudWatch, CloudFormation, Terraform, etc.) to host, scale, and manage model training and inference pipelines. Develop monitoring and alerting solutions for model latency, accuracy, data drift, and infrastructure health; integrate with Prometheus, Grafana, CloudWatch, or similar tools. Automate model versioning, artifact storage, and metadata tracking using Mlflow or SageMaker model registry. Collaborate 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. Document standards, best practices, and runbooks to enable smooth hand‑offs and knowledge sharing across teams. 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. 3+ years of experience in MLOps, DevOps, or cloud‑native engineering, preferably in a financial services or enterprise environment. 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). Deep knowledge of AWS cloud services (SageMaker, ECR/ECS/EKS, Lambda, Step Functions, S3, CloudWatch, IAM, CloudFormation/Terraform). Familiarity with CI/CD tools and infrastructure‑as‑code. Experience with ML lifecycle tools such as MLflow, Kubeflow, or SageMaker Pipelines. Strong problem‑solving skills and the ability to balance technical depth with business impact. Excellent communication and collaboration abilities; comfortable working with data scientists, product owners, and operations teams. 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.