Cloud Data Science Architect

2 weeks ago

Melbourne, Victoria, Australia beBeeDataScientist Full time $137,500 - $187,000
Job Description

This role involves working with clients on short to medium-term customer engagements on their ML & AI challenges using a data intelligent platform.

You will provide data science and cloud technology projects which require integrating with client systems, training, and other technical tasks to help customers get the most value out of their data.

The ideal candidate will have experience in building solution architecture, MLOPs frameworks, MVPs, and production-grade applications.

Responsibilities
  • Work on impactful data science and machine learning use cases including building solution architecture, MLOPs frameworks, MVPs, and production-grade applications.
  • Guide strategic customers as they implement transformational data science and Generative AI projects, including end-to-end design, build, and deployment of industry-leading and AI applications.
  • Consult on ML architecture and design; bootstrap or implement strategic customer projects that lead to a customer's successful understanding, evaluation, and adoption of the data intelligent platform.
  • Provide an escalated level of support for customer operational issues.
  • Work with the technical team, project manager, architect, and customer team to ensure the technical components of the engagement are delivered to meet customer needs.
Requirements
  • Experience in the following preferably in a customer-facing post-sales, technical architecture of consulting role:
    • ML Engineer: Build and maintain production-grade cloud infrastructure that supports the deployment of ML applications.
    • Data Scientist: Experience with the latest techniques in machine learning and Generative AI processing, including vector databases, fine-tuning LLMs, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI.
  • Comfortable writing code in either Python or Scala.
  • Experience with technical project delivery - managing scope and timelines.
  • Documentation and white-boarding skills.
  • Experience working with clients and managing conflicts.
  • Build skills in technical areas which support the deployment and integration of data intelligent solutions to complete customer projects.
  • Travel to customers 20-30% of the time.

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