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Senior AI Engineer

Senior AI Engineer

SAGL CONSULTING PTE. LTD.Singapore
3 days ago
Job description

Main Responsibilities

  • Lead the design and development of advanced machine learning and deep learning models for real-world business applications.
  • Collaborate cross-functionally with data scientists, product managers, and software engineers to identify, define, and implement AI use cases.
  • Drive the full model lifecycle : from data preprocessing and feature engineering to training, tuning, and production deployment.
  • Architect and implement robust MLOps pipelines , including CI / CD workflows, model versioning, monitoring, and automated retraining.
  • Mentor junior team members and contribute to best practices across the AI / ML function.
  • Continuously optimize and scale AI systems for performance, reliability, and cost-efficiency in production environments.
  • Stay ahead of the curve by exploring new research, frameworks, and tools in AI / ML, and proactively propose innovative applications.
  • Contribute to architectural decisions around data and ML infrastructure.

Job Requirements

Academic Qualifications :

  • Bachelor's or Master's in Computer Science, Data Science, AI, Engineering, or related fields
  • Experience & Technical Skills :

  • 5-10 years of hands-on experience in machine learning and AI solution development, with at least 3+ years working on models in production environments.
  • Strong track record of building, deploying, and maintaining ML systems at scale .
  • Expert-level Python skills and deep familiarity with ML / DL frameworks such as TensorFlow, PyTorch, Scikit-learn .
  • Solid grasp of machine learning algorithms, data engineering workflows, and software development best practices.
  • Experience working with cloud platforms (AWS, GCP, Azure) for training, experimentation, and deployment of ML models.
  • Hands-on experience with MLOps tools and practices : Docker, Kubernetes, Git, CI / CD, model monitoring.
  • Strong understanding of data pipelines , including use of orchestration tools (e.g., Airflow , Kafka ).
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