Participate in design considerations and implementation of highly scalable and fault tolerant distributed components using functional programming paradigm, APIs, abstractions and integration patterns to solve challenging Distributed Computing problems.

More Uses of the Distributed Computing Toolkit:

  • Be accountable for integrating systems related to data acquisition and data analysis for users in multiple groups/disciplines.
  • Identify: GPU processing, Distributed Computing, highly parallel coding, cloud computing, machine learning, visualization, system modelling and simulation to achieve results.
  • Guide: algorithmic complexity, deep learning performance analysis and profiling, Distributed Computing, AI accelerators, gpus.
  • Ensure you win; build new predictive models from scratch, from exploratory data analysis to production model, using Python.
  • Ensure you address; lead software quality assurance related activities, as reviewing source code for compliance with style guidelines.
  • Develop methods and strategy to minimize impact on customer areas, migrate and transition to operational service delivery teams for life cycle support.
  • Manage: design and build innovative technologies in a large Distributed Computing environment and help lead fundamental changes in the industry.
  • Evaluate: client server, web based Distributed Computing, and microcomputer programming concepts and issues.
  • Initiate: in partnership with architecture and development, future proof test automation with new feature capabilities and new offerings that drive quality engineering and product delivery value.
  • Arrange that your business develops and applies advanced methods, theories, and research techniques in the investigation and solution of complex and advanced software applications and problems.
  • Ensure frequent communication with other stakeholders to drive use case development and manage expectations on model limitations and lead times.
  • Make sure that your team complies; continuous improvement champion solutions that tie production leakage tie back to the Quality Engineering process.
  • Ensure you chart; understand the changing business needs of your organization/projects and recommend viable strategies for the future.
  • Organize: artificial intelligence and machine learning to design, prototype, and build solutions to business problems.
  • Ensure you are resilient and able to cope with change while finding ways to advance the automation cause in any platform or team you support.
  • Organize: execution of machine learning projects to address specific business problems determined by consultation with business partners.
  • Warrant that your enterprise performs analytics in support of the identification and understanding observed business outcomes.
  • Perform data analysis, feature engineering and advanced methods to prepare and develop decisions from data.
  • Be a thought leader and partner in the development and execution of the Enterprise Technology Strategy.
  • Identify: proactively collaborating with business partners to determine identified population segments and developing actionable plans to enable the identification of patterns related to quality, use, cost and other variables.
  • Supervise: leverage simple to advanced data techniques to support the team to deliver data analytic products for your organization.
  • Specify, design and implement modest changes to existing software architecture to meet changing needs.
  • Be accountable for developing proprietary machine learning algorithms to build customized solutions that go beyond standard industry tools and lead to innovative solutions.
  • Maintain and/or develop automation tools/framework to automate functional and regression test scenarios.
  • Analyze complex business and technical problems related to the implementation of new technology and/or the customization of existing technologies.
  • Ensure you delegate; lead the design, implementation and support for the Enterprise Identity Rights Management program architecture, infrastructure, capabilities, components and standards.
  • Develop a protocol for test infrastructure, tooling, determine how to test metrics and test outcomes are collected and managed.
  • Be accountable for piping and processing massive data streams in Distributed Computing environments as Spark to facilitate analysis.
  • Formulate: actively seek opportunities to continuously improve the quality of the applications and platform.
  • Ensure you collaborate; Distributed Computing, object oriented development, data cleansing, algorithms and data structures.

 

Categories: Articles