ML Prototyping Architect
London, UK
6d ago

Amazon Web Services (AWS) EMEA is looking for a Solutions Architect to be part of the Prototyping Competence for Advanced Analytics.

The Advanced Analytics Prototyping team includes data and analytics developers, data scientists and machine learning specialists working with AWS customers to support designing complex analytics environments, machine and deep learning solutions leveraging AWS services.

The team is part of EMEA Prototyping Labs and will work in close partnership with the Sales and Business Development teams to enable large-

scale customer use cases and drive the adoption AWS Advanced Analytics services.

The Prototyping Team engages directly with the customers’ development team, understand their specific business and technology challenges in the area of data and advance analytics, support hands-

on building machine learning / deep learning prototypes and transfer knowledge on specific AWS services. We are expecting the Team Manager to be both a team leader and a hands-on developer.

Prototyping engagements will usually require to travel to Customers across EMEA and work 2-3 weeks on-site in a temporary Lab-like environment.

The Advanced Analytics Prototype Solutions Architect will collaborate closely with the wider Prototyping Labs Competencies, e.

g. IoT, Virtual Reality / Augmented Reality, Voice and Image Recognition to integrate AWS Deep Learning and Machine learning into more complex customer prototypes.

  • Strong understanding and experience in the field of Data Lakes, BI, Machine Learning, Deep Learning and related technologies.
  • Previous experience of developing AI models in real-world environments and integrating AI / ML, and other services, into large-
  • scale production applications.

  • Strong verbal and written communications skills, as well as the ability to work effectively across internal and external organizations and virtual teams
  • Ability to think strategically about business, product, and technical challenges in an enterprise environment. Understanding of Agile methodologies, and the ability to apply these practices to Analytics projects.
  • Data science background and experience manipulating / transforming data, model selection, model training, cross-validation and deployment at scale.
  • Experience with Machine and Deep Learning toolkits such as MXNet, TensorFlow, Caffe and Torch.
  • Experience with statistical / mathematical software (e.g. R, Weka, SAS, Matlab)
  • Proficiency with programming languages such as Python, Scala and R
  • Experience with NoSQL is strong plus
  • Experience with AWS services related to AI / ML highly desirable, particularly Amazon EMR, AWS Lambda, Machine Learning, AWS IoT & Greengrass, Amazon DynamoDB, Amazon S3, Redshift, Amazon EC2 Container Service, etc.
  • Apply
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