Lead Machine Learning Engineer

4 weeks ago

Singapur, Singapore Unison Consulting Pte Ltd Full time
Job Title: Lead Machine Learning Engineer

As a key member of our team at Unison Consulting Pte Ltd, you will play a pivotal role in operationalizing machine learning models developed by our data scientists. Your primary responsibilities will include:

Main Responsibilities:
  • Conduct thorough reviews of ML models to ensure compliance with our platform's governance principles, including versioning, data/model lineage, and code best practices. Provide constructive feedback to data scientists for potential improvements.
  • Develop and implement pipelines for continuous operation, feedback, and monitoring of ML models, leveraging best practices from the CI/CD vertical within the MLOps domain. This includes monitoring for data drift, triggering model retraining, and setting up rollbacks.
  • Optimize AI development environments (development, testing, production) for usability, reliability, and performance.
  • Collaborate closely with the infrastructure and application development team to understand the best method of integrating ML models into enterprise applications (e.g., transforming resulting models into APIs).
  • Work with data engineers to ensure data storage (data warehouses or data lakes) and data pipelines feeding these repositories and the ML feature or data stores are working as intended.
  • Evaluate open-source and AI/ML platforms and tools for feasibility of usage and integration from an infrastructure perspective. Stay updated about the newest developments, patches, and upgrades to the ML platforms in use by our data science teams.
Requirements:
  • At least 10 years of experience as a ML Engineer, working with Python, ML libraries, and large-scale data processing frameworks.
  • Advanced proficiency in Python used both for ML and automation tasks.
  • Experience in working with ML libraries such as Pandas, NumPy, H2O, or TensorFlow.
  • Experience in working with large-scale data processing frameworks, such as Hadoop, Spark, or Dask.
  • Knowledge of OpenShift/Kubernetes is a must-have.
  • Good knowledge of Bash and Unix/Linux command-line toolkit is a must-have.
  • Hands-on experience building CI/CD pipelines orchestration by Jenkins, GitLab CI, GitHub Actions, or similar tools is a must-have.
  • Knowledge in the operationalization of Data Science projects (MLOps) using at least one of the popular frameworks or platforms (e.g., Kubeflow, AWS SageMaker, Google AI Platform, Azure Machine Learning, DataRobot, Dataiku, H2O, or DKube).
  • Knowledge of Workflow Orchestrator, such as Airflow or Ctrl-M.
  • Knowledge of Logging and Monitoring tools, such as Splunk and Geneos.
  • Experience in defining the processes, standards, frameworks, prototypes, and toolsets in support of AI and ML development, monitoring, testing, and operationalization.
  • Experience in ML operationalization and orchestration (MLOps) tools, techniques, and platforms. This includes scaling delivery of models, managing and governing ML Models, and managing and scaling AI platforms.
  • Knowledge of cloud platforms (e.g., AWS, GCP) would be an advantage.

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