Cloud AI Solutions Architect

4 days ago

Sydney, New South Wales, Australia H2o Full time

We are seeking a highly skilled and motivated Cloud AI Solutions Architect to join our team in Australia.

Company Overview

H2 O.ai is at the forefront of the rapidly evolving MLOps landscape, leveraging MLOps to transform machine learning models from isolated tools into robust cloud-native services. Our approach is firmly rooted in Kubernetes, positioning us at the cutting edge of cloud solutions.

About the Role

This is an exciting opportunity for a seasoned Cloud AI Solutions Architect to design and implement scalable, reliable, and high-performance cloud-based AI solutions using H2 O's cutting-edge technology. As a key member of our team, you will work closely with cross-functional teams to architect, develop, and deploy AI-driven applications that drive business value.

Key Responsibilities
  • Deliver technical professional services to customers
  • Work closely with H2 O Data Scientists to advise and develop end-to-end machine learning solutions for customer requirements
  • Integrate H2 O products with customer data sources for model training
  • Implement end-to-end ML data flow pipelines to streamline data science solutions to business problems
Requirements
  • Bachelor's or higher education degree in Computer Science/Engineering, Data Science, Statistics, or related field
  • Strong background in cloud computing (AWS, Azure, Google Cloud) and containerization (Docker, Kubernetes)
  • Experience with big data technologies such as Hadoop, Spark, or NoSQL databases
  • Proficient in Python or R for data science and experience with REST API development
  • Excellent understanding of machine learning algorithms and techniques, including supervised/unsupervised learning, clustering, decision tree learning, neural networks, etc.
  • Familiarity with DevOps practices and tools such as Git, Jenkins, Argo CD
Estimated Salary Range: AU$120,000 - AU$180,000 per annum

The estimated salary range is based on industry standards and takes into account factors such as location, experience, and qualifications.

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