- Opportunity to Work on cutting edge technology
- Transformational project in a fast paced innovative environment
- Generous Daily rate and long term engagement
Swift execution of Big Data processing, Machine Learning and ML Ops best practice terabytes of data from the largest Australian retail is what this role is about. Working as part of the ML Development chapter and closely collaborating with Data Scientists, be prepared to build Cloud based cutting edge ML platforms, pipelines and experiment with Big Data and ML tech available on the market.
The team has a culture of its own, which involves an innate desire to do things differently trying to find the best solution for business needs and push tech to the limit. No matter how experienced you are, be prepared to learn a lot!
The primary goal of this role is to help design and build a state of the art Machine Learning. Platform working collaboratively with Data Scientists and other ML developers. The role will report to a Machine Learning Chapter Lead. There is an option to be located in the company's Surry Hills or Norwest offices in Sydney with additional work from home options available.
- Hands-on technical leadership in design and implementation of state of the art Machine Learning platform
- Growth of ML chapter capability through pioneering new tech and approaches to improve time to market, reliability and performance of Data Science products
- Mentoring less experienced ML developers
- Stakeholder management across different disciplines
- Bachelor or above degree in Computer Science
- Commercial software development experience
- Good communication and stakeholder management skills
- Kubernetes and Argo/Kubeflow experience will be advantageous
- Solid knowledge of data structures, algorithms and system design with focus on reliable high throughput batch and streaming solutions
- Solid experience with streaming technologies like Kafka, Pubsub or Kinesis will be advantageous
- Practical knowledge of various classes of ML algorithms, techniques and packages
- Experience of designing and implementing complex cloud solutions
- Experience of building production grade ML feature stores, model stores or ML pipelines in GCP or AWS
- GCP Cloud Engineer or above level of certification
- Solid ML Ops, SRE or DevOps experience
For more information please feel free to contact email@example.com