OCBC
Financial Services
MachineLearningOpsEngineer
Neural analysis suggests this role is
optimal for Mid candidates.
“Machine Learning Ops Engineer at OCBC. Skills: MLOps, AWS, Python, Kubernetes. Design MLOps pipelines. Build MLOps pipelines”
What You'll Achieve.
Turn experimental models into production-ready services; Ensure models run securely, efficiently, and with high availability; Deliver robust, production grade AI systems; Continuously monitor and improve model performance
Industry & Context.
Problem-solving skills; Ability to balance technical depth with business impact
What They're Looking For.
Must Have
Degree in Computer Science, Software Engineering, Data Engineering, or a related field, 3+ years of experience in MLOps, DevOps, or cloud-native engineering, Proficiency in Python, Proficiency in model inferencing stack (Ray, VLLM, SGLang), Hands-on experience with containerization (Docker), Hands-on experience with 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, Familiarity with infrastructure-as-code, Experience with ML lifecycle tools (MLflow, Kubeflow, or SageMaker Pipelines)
Nice to Have
Experience in a financial services or enterprise environment
What You'll Do.
Design MLOps pipelines
Build MLOps pipelines
Maintain MLOps pipelines
Implement CI/CD workflows
Containerize ML workloads
Orchestrate workloads on Kubernetes
Develop monitoring solutions
Automate model versioning
Package models as services
Ensure security of ML pipelines
Document standards and best practices
How You'll Work.
Team & Collaboration
Collaborate with cross-functional teams; Collaborate with stakeholders to understand business needs; Communicate effectively with technical stakeholders; Communicate effectively with non-technical stakeholders; Work with data scientists; Work with product owners; Work with operations teams
Communication Scope
Excellent communication
Full Job Description
# **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 technic
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