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Machine Learning Engineer

4 months ago

Singapur, Singapore NTT SINGAPORE PTE. LTD. Full time
Roles & Responsibilities

About the Role:

  • Review ML models for compliance with platform governance principles including versioning, data lineage, and code best practices. Provide feedback to data scientists for enhancements.
  • Develop pipelines for continuous operation, feedback, and monitoring of ML models following CI/CD best practices in the MLOps domain. This includes monitoring data drift, triggering model retraining, and implementing rollbacks.
  • Optimize AI development environments for usability, reliability, and performance.
  • Collaborate closely with infrastructure and application development teams to integrate ML models into enterprise applications effectively (e.g., transforming models into APIs).
  • Work with data engineers to ensure smooth operation of data storage (data warehouses or data lakes) and pipelines feeding these repositories along with the ML feature or data stores.

Required Technical Skills:

  • Proficiency in Python for ML and automation tasks.
  • Solid understanding of Bash and Unix/Linux command-line toolkit.
  • Hands-on experience building CI/CD pipelines using tools like Jenkins, GitLab CI, GitHub Actions.
  • Knowledge of OpenShift / Kubernetes.
  • Familiarity with ML libraries such as Pandas, NumPy, H2O, TensorFlow.
  • Experience in operationalizing Data Science projects (MLOps) with frameworks like Kubeflow, AWS Sagemaker, Azure ML, etc.
  • Understanding of Distributed Data Processing frameworks like Spark, Dask.
  • Knowledge of Workflow Orchestrator tools like Airflow, Ctrl-M.
  • Proficiency in Logging and Monitoring tools such as Splunk, Geneos.
  • Expertise in defining processes, standards, and frameworks to support AI and ML development, testing, and operationalization.
  • Hands-on experience in ML operationalization and orchestration tools and platforms, including scaling model delivery, governance, and managing AI platforms.
  • Experience with cloud platforms like AWS, GCP is a plus.
  • Evaluate open-source AI/ML platforms and tools for infrastructure integration and feasibility. Stay updated on developments and upgrades in ML platforms used by data science teams.

If you are interested, kindly send your resume to the provided email address.

Showcase Your Skills:

TensorFlow
Machine Learning
Airflow
Kubernetes
Azure
Pipelines
SageMaker
Artificial Intelligence
Application Development
Reliability
Logging
Python
Artificial Intelligence Application
Data Science
Orchestration
GitHub