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Company Overview
A digital bank based in Singapore, backed by a consortium of Grab Holdings Inc. and Singtel, aims to offer improved banking services to everyday consumers and small businesses through technology.
Job Title: Lead Specialist – ML Ops Engineer
Job Description:
A leading organization is seeking a highly skilled and experienced Lead Specialist – ML Ops Engineer to drive the development and implementation of their machine learning infrastructure. You will be instrumental in building robust and scalable MLOps pipelines to support the deployment and management of cutting-edge machine learning models.
Responsibilities:
Design, develop, and implement end-to-end MLOps pipelines for machine learning projects, including data pipelines, model training environments, and deployment mechanisms using cloud services and container orchestration tools.
Drive the implementation of automation solutions for continuous integration, continuous delivery, and continuous training (CI/CD/CT) of machine learning models to streamline the development and deployment processes.
Collaborate with machine learning engineers to understand model requirements and optimize deployment processes.
Implement and oversee monitoring solutions for machine learning applications in production, ensuring high availability, performance, and reliability. Lead incident response, root cause analysis, and implement robust fixes.
Drive initiatives to continuously assess and optimize the performance of machine learning models’ infrastructure in production, including resource allocation, cost reduction, and latency improvements.
Manage the end-to-end lifecycle of machine learning models in production, including updates, version control, and retirement of models that no longer meet the performance criteria.
Establish and maintain comprehensive documentation for operational procedures, system configurations, and best practices.
Develop automation scripts and tools to improve the efficiency and reliability of ML workflows.
Skills and Knowledge:
7+ years of strong practical experience with AWS services, particularly those related to computing, storage, networking, and security.
Strong experience with Containerization Technology such as Docker, Kubernetes & Helm.
Deep understanding of MLOps principles and experience with tools such as MLflow, Kubeflow, or Vertex AI/SageMaker.
Proficiency in infrastructure as code (IaC) using Terraform, or similar.
Solid background in CI/CD methodologies and tools (e.g., GitLab CI/CD).
Programming skills in Python, with familiarity in ML libraries and frameworks (TensorFlow, PyTorch).
Demonstrated experience in deploying and maintaining ML models in a production environment.
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