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Open nowPosted 12 days ago

Machine Learning Engineer

MyCareersFuture94,028 open roles

Pay
SGD 7,500 – SGD 13,500 a month
Where
Singapore
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Your applicationOpen nowMachine Learning EngineerMyCareersFuture · Singapore
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  2. 3.3%3 days
  3. 7.7%7 days
  4. 14.0%14 days
  5. 33.7%30 days
This job: posted 12 days ago

The posting

We are seeking a skilledmachine learning platform engineer (MLOps) to join our agile platform teamwhich is part of our ML & AI ART. You drive the orchestration of advancedagentic workflows to enable autonomous, AI-driven systems. You will beresponsible for engineering robust data pipelines, establishing comprehensivemodel management lifecycles, overseeing all foundational platform-level AIintegrations – including engineering a robust library of AI skills for agentuse. Design, develop and deploy machine learningsolutions and services

  • Implement end-to-end machine learningpipelines from data ingestion to training and model serving Operationalize LLMs, embeddings, andmulti-agent systems in real-world applications
  • Manage the machine learning and modellifecycle (experimentation, registry, deployment)
  • Oversee the model promotion lifecycle,coordinating validation gates and approval workflows to safely deploy new modelversions from stating to production
  • Containerize applications using Docker andorchestrate them via Kubernetes
  • Build and maintain CI/CD pipelines for MLmodels and LLM applications
  • Design and implement production grade RAGsystems
  • Advanced proficiency in Python programming with a focus on writing clean, testable and efficient code
  • DevOps & Containers: Proficient with Docker for containerization and working knowledge of Kubernetes (k8s) for orchestration
  • Practical understanding of GPU architecture and cloud compute instances to optimize resource allocation for training and inference workloads
  • MLOPS tools: hands on experience with MLflow (or similar tools like weights & biases) for experiment tracking and model registry
  • Proven experience working with Large Language Models (LLMs)
  • Good understanding of AI agents & agentic workflows, LLM orchestration frameworks and reasoning patterns
  • Experience with data preprocessing, feature engineering, and model selection and evaluation techniques
  • Hands-on experience with CI/CD pipelines (GitLab, Jenkins)
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