Senior Ai/ml Consultant, Aws Professional Services

Amazon Amazon · Big Tech · Perth, WA, Australia · Applied Science

Senior AI/ML Consultant role focused on helping enterprise customers develop and deploy ML and GenAI solutions using AWS services. Responsibilities include understanding business challenges, designing solutions, building and validating models, and collaborating with engineering teams for production deployment.

What you'd actually do

  1. Accurately understand the customer's business challenges and needs, structure the problems, and organise them as business requirements.
  2. Extract the IT requirements necessary to realise the business requirements and select candidate AI/ML services and algorithms. In addition, you will work with specialist roles in related technical fields to build and verify ML solutions, as wellas help develop an architecture that combines the necessary AWS services for achieving the customer's goals.
  3. Assist our customer on developing ML and GenAI projects from start to finish, providing technical sales support, conducting exploratory data analysis, building and validating ML and/or GenAI solutions, deploying validated solutions on their supporting infrastructure, and providing training to our customers.
  4. Collaborate with ML Engineers Cloud Architect and Application Developer to build a production ready ML or GenAI solution
  5. To support the above, you will work with a wide range of IT tools, including AWS services (e.g. Amazon Bedrock, Amazon Sagemaker, Amazon AgentCore), Git and Docker, as well as with a range on Ml and GenAI framework such as Strands-Agents, pytorch, transformers, etc

Skills

Required

  • 5 to 8 years of experience as a data scientist
  • experience building ML model
  • experience prompting GenAI model
  • AWS or similar cloud technologies

Nice to have

  • Master's degree in computer science, machine learning, operations research, statistics, mathematics, or other fields
  • Deep technical skills and business savvy
  • Skills in creating experimental and analytical plans for data modelling processes
  • ability to accurately determine cause-and-effect relationships using basel

What the JD emphasized

  • building ML model
  • prompting GenAI model

Other signals

  • customer-facing
  • solution building
  • AWS ML services