Job Description

Locations

• Singapore, Singapore, Singapore 569141

Salary

Skills Required

• MLOps engineeringintermediate
• automating machine learning pipelinesintermediate
• securing ML modelsintermediate
• securing data infrastructuresintermediate
• AI/ML operationsintermediate
• operational excellenceintermediate
• machine learning engineering processesintermediate
• integration of machine learning development and production operationsintermediate
• model deploymentintermediate
• infrastructure requirementsintermediate
• efficiencyintermediate
• scalabilityintermediate
• securityintermediate
• reliabilityintermediate
• machine learning lifecycleintermediate
• cloud platformsintermediate
• ML automationintermediate
• model monitoringintermediate
• ML infrastructure managementintermediate
• Designing ML engineering platformsintermediate
• Designing toolingintermediate
• building machine learning modelsintermediate
• training machine learning modelsintermediate
• deploying machine learning modelsintermediate
• operating machine learning modelsintermediate
• Developing automation for model training pipelinesintermediate
• Developing automation for deployment workflowsintermediate
• Developing automation for model monitoringintermediate
• Developing automation for ML operational tasksintermediate
• Monitoring ML model performanceintermediate
• Monitoring data driftintermediate
• Monitoring model accuracyintermediate
• Monitoring system availabilityintermediate
• remediating ML issuesintermediate
• Collaborating with data science teamsintermediate
• Collaborating with ML engineering teamsintermediate
• model lifecycle managementintermediate
• ML governanceintermediate
• model securityintermediate
• compliance requirements for AI/ML systemsintermediate
• Mentoring MLOps engineersintermediate
• ML best practicesintermediate
• model deployment strategiesintermediate
• MLOps design approachesintermediate

Required Qualifications

• 3+ years in MLOps Engineering or ML Platform Engineering roles (experience, 3 years)
• 2+ years in Data Engineering, Software Engineering, or Data Science roles (experience, 2 years)
• Proficiency with containerization and orchestration technologies such as Docker, Kubernetes, or ML-specific platforms like Kubeflow. (experience)
• Proficiency with programming and scripting languages such as Python, R, SQL, or Bash, with strong Python expertise for ML workflows. (experience)
• Experience with infrastructure-as-code and ML pipeline tools (e.g. Terraform, MLflow, Airflow, Prefect). (experience)
• Proficiency in CI/CD for ML pipelines using Jenkins, or ML-specific platforms like Kubeflow Pipelines. (experience)
• Proficiency with ML monitoring and observability technologies such as MLflow, Prometheus, Grafana, or model drift detection tools. (experience)
• Proficiency with designing and operating ML workloads in a public cloud environment (AWS SageMaker, GCP Vertex AI, etc.). (experience)
• Experience integrating ML governance, model security, and compliance practices into all stages of the ML lifecycle. (experience)
• Continuously work with data science and ML engineering teams to improve model reliability, implementing actionable ML monitoring frameworks and participate in ML system on-call. (experience)
• Analytical & problem solving skills with understanding of ML concepts, ability to communicate technical ML concepts clearly. (experience)
• Excellent communication and collaboration abilities, with experience bridging data science and engineering teams. (experience)

Preferred Qualifications

• Bachelor's Degree in Computer Science, Data Science, Machine Learning, or equivalent in Engineering (degree in computer science)
• 4+ years in MLOps Engineering, ML Platform Engineering, or equivalent ML operational experience (experience, 4 years)
• 3+ years in Software Engineering, Data Engineering, or ML Engineering roles with focus on production ML systems (experience, 3 years)
• Expertise in operating and integrating with cloud ML services such as AWS SageMaker, EKS, CloudWatch, S3, or equivalent GCP ML services (experience)
• Experience with large-scale distributed ML systems and data platforms such as Spark, Snowflake, Ray, or Kubernetes-based ML frameworks (experience)
• Experience with ML model governance, AI compliance frameworks, and preparing for ML-specific audits (model validation, bias testing, explainability requirements) (experience)

Responsibilities

• We are looking for a world-class MLOps Engineer who is passionate about operational excellence through automation and machine learning engineering processes. As an MLOps Engineer, you will play a crucial role in ensuring the seamless integration of machine learning development and production operations, to deliver best in class and highly available ML systems.
• You will work with data science, data engineering, and AI/ML engineering teams to understand model deployment and infrastructure requirements, while promoting efficiency, scalability, security, and reliability throughout the machine learning lifecycle. Your expertise in cloud platforms, ML automation, model monitoring, and ML infrastructure management will be crucial to the success of our AI/ML projects.
• Your responsibilities will include:
• - Designing ML engineering platforms and tooling that simplify the process of building, training, deploying, and operating machine learning models at scale.
• - Developing automation for model training pipelines, deployment workflows, model monitoring, and ML operational tasks.
• - Monitoring ML model performance, data drift, model accuracy, and system availability, and remediating as necessary.
• - Collaborating with data science and ML engineering teams on finding operationally sustainable solutions to model deployment and lifecycle management challenges.
• - Provide guidance to the engineering organization on navigating ML governance, model security, and compliance requirements for AI/ML systems.
• - Mentoring other MLOps engineers around ML best practices, model deployment strategies, and MLOps design approaches.

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