Machine Learning Engineers
|Job Title:||Machine Learning Engineers|
|Location:||Canberra, Australian Capital Territory|
|Contact Name:||Chins Christopher|
|Job Published:||March 12, 2022 12:00|
ASAP Start | Canberra Location
Initial contract for 12 months with 12 months extension
Current baseline clearance
The client is seeking experienced machine learning engineers to drive the design and implementation of MLOps using a combination of Azure Databricks, Azure Machine Learning, Azure Kubernetes and other Azure products. The ideal candidate is someone who can communicate clearly and work with data scientists, Azure platform engineers and data architects to establish robust foundational MLOps and MLOps best practices.
The key responsibilities include:
- Work with data scientists to convert existing model development code into models which can be served robustly in production. Python and Apache Spark experience is required.
- Work with data architects and Azure platform engineers to adopt the design of the MLOps architectural patterns to flexibly support different ML model serving requirements across the department.
- Implement code testing and deployment principles in Dev/Test/Prod environments and using version control via GIT and Azure DevOps.
- Participate in code review and communicate any designs and code clearly with appropriate documentation of ideas and processes.
- Work within an Agile framework using Azure DevOps and be accountable for planning, analysis, design, development, and delivery activities.
The successful applicant would be required to have:
- Demonstrated MLOps experience using Azure Databricks, Azure Machine Learning, Azure Kubernetes and other Azure products:
- Extensive experience in MLOps, including creating CI/CD pipelines for model deployment, model performance and data drift monitoring using any cloud-based tools.
- At least 6 months experience using Azure Databricks, Azure Machine Learning, Azure Kubernetes and other Azure products for MLOps.
- In-depth experience in using Python and Apache Spark distributed libraries i.e. PySpark, Spark ML.
- Demonstrated experience working with data scientists and platform engineers to convert experimental ML code into production-ready ML models for serving.
- Extensive experience writing and testing ML code in production.
- Extensive experience applying testing and deployment principles in Dev/Test/Prod environments and using version control via GIT and Azure DevOps
- Ability to document conceptual ideas using Azure DevOps, Wikis or other forms of documentation.
- Strong communication skills, including an ability to describe the reasoning for monitoring specific ML model and data metrics to multiple stakeholders.
- Experience with MLOps architectural designs, including the ability to adopt existing MLOps designs and work with data architects and IT security teams.
- Demonstrated understanding and ability to explain MLOps architectural components to multiple stakeholders.
- Well-developed team collaboration and communication skills:
- Demonstrated ability to work under limited direction and accountable for completion of work
- Demonstrated ability to work closely and effectively within an Agile multi-disciplinary team environment using Azure DevOps.
- Demonstrated ability to adapt and work in a constantly changing environment
About the Company:
FinXL fosters a high-performing, inclusive workplace built on a foundation of excellence, respect and dignity. We take corporate social responsibility seriously through our ongoing activities with communities and staff involvement in these efforts. We are committed to environmentally friendly practices in both our own operations and our work with clients.
To be considered please send applications or contact Chins on 02 6243 6409 to discuss further.
FinXL does not accept unsolicited resumes or appreciate unsolicited calls from recruitment agencies.
FinXL encourages applications from Aboriginal and Torres Strait Islander people.
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