CLOUD MACHINE LEARNING ENGINEER (AWS)

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Skills you'll use

What you'll do

• Design and develop machine learning systems and implementation patterns.
• Automate predictive model software, including model training.
• Productionise data science prototypes and develop machine learning applications aligned to data science requirements.
• Facilitate the flow of data between ML/AI models and the organisation’s data systems.
• Enhance data pipelines to ensure data is clean, accurate, and optimised for machine learning models.
• Partner with the architecture team to evolve Big Data platform capabilities (reusable assets/patterns) and components to meet business requirements and objectives.
• Research, investigate, and evaluate new technologies and methods to improve delivery and sustainability of machine learning applications and services.
• Contribute to defining best practice for agile development of applications running on the Big Data platform.

Who can apply

• Experience managing the development lifecycle for agile software development projects (Kanban or Scrum exposure).
• Robust data modelling and data architecture skills.
• Knowledge of Big Data frameworks such as Hadoop, Spark, Hive, Yarn, and Airflow.
• Experience with distributed ML frameworks (for example H2O and/or TensorFlow) and various ML libraries.
• Proven experience creating and deploying end-to-end ML pipelines in production, including MLOps.
• Programming experience in Java and Python.
• Experience with containerisation (Docker/Kubernetes) or cloud alternatives is advantageous.
• Mandatory working experience with AWS services including: SageMaker Pipelines, SageMaker Studio, CloudFormation, CloudTrail, SNS, EventBridge, CodePipeline, CodeBuild, CodeCommit.
• Experience with other distributed technologies, NoSQL databases, and streaming technologies is desirable.
• Strong written and verbal communication, with excellent interpersonal and collaboration skills.

About Vodafone Intelligent Solutions (VOIS)

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