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Ai Engineer

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Nicoll Curtin

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Location:
Singapore , Singapore

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Category:
IT - Software Development

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Contract Type:
Not provided

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Salary:

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Job Description:

We are seeking a skilled machine learning platform engineer (MLOps) to join our agile platform team which is part of our ML & AI ART. In this role, Bridge the gap between experimental data science and production-grade systems. You'll contribute across the entire lifecycle - from concept to deployment - and collaborate closely with cross-functional teams to deliver high-quality digital solutions. Further, you drive the orchestration of advanced agentic workflows to enable autonomous, AI-driven systems. You will be responsible for engineering robust data pipelines, establishing comprehensive model management lifecycles, overseeing all foundational platform-level AI integrations – including engineering a robust library of AI skills for agent use.

Job Responsibility:

  • Design, develop and deploy machine learning solutions and services
  • Implement end-to-end machine learning pipelines from data ingestion to training and model serving
  • Operationalize LLMs, embeddings, and multi-agent systems in real-world applications
  • Manage the machine learning and model lifecycle (experimentation, registry, deployment)
  • Oversee the model promotion lifecycle, coordinating validation gates and approval workflows to safely deploy new model versions from stating to production
  • Containerize applications using Docker and orchestrate them via Kubernetes
  • Build and maintain CI/CD pipelines for ML models and LLM applications
  • Collaborate with data scientists to refactor research code into production-ready Python code
  • Monitor model performance, data drift, and performance in production
  • Assess and integrate AI solutions ensuring optimal performance and reliability
  • Design and implement production grade RAG systems
  • Collaborate with infrastructure teams, data engineers, data scientists, and other stakeholders to integrate machine learning solutions into existing systems and processes
  • Participate in code reviews, testing, and debugging to ensure the quality and reliability of machine learning solutions

Requirements:

  • Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, Statistics, or a related field
  • 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)
  • Knowledge of statistical and mathematical concepts relevant to machine learning, such as probability, linear algebra, and optimization
  • Excellent problem-solving and debugging skills, with the ability to identify and resolve issues quickly and effectively
  • Relevant work experience in machine learning, data science or a related field

Additional Information:

Job Posted:
April 23, 2026

Employment Type:
Fulltime
Work Type:
On-site work
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