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Urgent! Intermediate Machine Learning Engineer Job Opening In Singapore, Singapore – Now Hiring KRIS INFOTECH PTE. LTD.

Intermediate Machine Learning Engineer



Job description

Job Description:

  • Collaborate with data scientists and business stakeholders to understand use cases and define ML solution; work on Proof of Concepts wherever needed
  • Engineer and deploy ML models into production using MLOps best practices (model versioning, monitoring, CI/CD, etc.).

  • Build & maintain data pipelines and model performance for scalability and maintainability.

  • Ensure all models adhere to organizational AI policies, responsible AI practices, and audit requirements.

  • Support data exploration, feature engineering, and occasional model building where needed.

  • Automate model retraining, testing, and monitoring to ensure performance over time.

  • Document ML workflows, governance checkpoints, and risk assessments.

  • Partner with DevOps, IT, and security teams to integrate solutions into enterprise platforms.

  • The position requires autonomy and reliability in performing duties while maintaining close communication with rest of stake-holders.

Requirements:

Mandatory:

  • Have Master's degree in the field of AI / ML and data science with proven ability to design and develop models
  • 6+ years of experience in data science and machine learning, with at least 3+ years in ML engineering roles.

  • Proven experience in end-to-end ML lifecycle: data wrangling, model development, deployment, and monitoring.

  • Strong programming skills in Python (pandas, scikit-learn, TensorFlow/PyTorch, etc.).

  • Strong knowledge in NoSQL databases (any experience in Graph database is desirable)
  • Experience with MLOps tools: MLflow, TFX, Airflow, Kubeflow, or similar.

  • Familiarity with cloud platforms (GCP, AWS, or Azure) for ML deployment.

  • Knowledge of data science techniques including supervised/unsupervised learning, NLP, time series, etc.

  • Experience with CI/CD pipelines and containerization (Docker, Kubernetes).

  • Strong understanding of AI governance, model risk management, and regulatory requirements in AI.

  • Ability to communicate technical concepts to non-technical stakeholders.

Preferred skills:

  • Experience with Responsible AI frameworks and bias/fairness testing.

  • Exposure to feature stores, model registries, and data versioning.

  • Knowledge of data privacy, anonymization, and compliance in regulated industries (e.g., banking, healthcare).

Other Professional Skills and Mind-set:

  • Ability and willingness to learn and adopt new technologies.

  • Strong organizational and communication skills.

  • Strong analytical and problem solving skills.

  • Awareness of various software development procedures.

  • Ability to follow defined procedures.

  • Understanding and respect of cultural diversity.


Required Skill Profession

Other General



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