Machine Learning Deployment Specialist

4 days ago

Singapore beBeeMl Full time $80,000 - $120,000
Job Title

Machine Learning Deployment Specialist.

Job Description

We are seeking a seasoned Machine Learning Deployment Specialist to drive the successful deployment, monitoring, and optimization of machine learning models in production. This role ensures the reliability, scalability, and efficiency of our ML infrastructure while supporting AI-driven solutions development.

Key Responsibilities
  • Design, build, and maintain scalable ML deployment pipelines for real-time and batch inference.
  • Manage and optimize cloud-based ML infrastructure for high availability and cost efficiency.
  • Implement monitoring, logging, and alerting systems to track model performance and data drift.
  • Automate model training, evaluation, and deployment using CI/CD pipelines.
  • Ensure compliance with MLOps best practices, including versioning and governance.
  • Collaborate with data scientists and engineers to transition models into production.
Required Skills and Qualifications

The ideal candidate will have hands-on experience with MLOps platforms (e.g., MLflow, Kubeflow, SageMaker), strong expertise in cloud services (AWS, GCP, Azure), proficiency in containerization (Docker, Kubernetes) and infrastructure as code (Terraform, CloudFormation). Additionally, they will have experience in building CI/CD pipelines for ML models, programming skills in Python, Go, or Shell scripting, familiarity with data versioning and monitoring tools (DVC, Prometheus), understanding of feature stores and data management for ML workflows, strong problem-solving skills, proactive attitude, excellent collaboration and communication skills, and fluency in Mandarin for effective team communication.

Benefits

As a Machine Learning Deployment Specialist, you will have the opportunity to work with cutting-edge MLOps and AI deployment technologies in a growing industry. You will be part of a dynamic, innovative team focused on AI and cloud solutions, gain exposure to end-to-end ML workflows, and have opportunities for professional growth in cloud computing, automation, and AI infrastructure.

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