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Ai consultant for canada project

Nihilent Analytics

This is a Full-time position in Montreal, QC posted January 14, 2020.

The assignment is expected to be for approximately six months, located in Montreal, Canada and we are looking for the resource as soon as possible.

Below are some of the job specifications.

Primarily, the client is using Azure resources for AI.

So knowledge of Azure and AI components on Azure will be needed.

They have a team of data scientists, data engineers, web app developers, etc., so there is no coding involved for this role.

The client is currently using the below resources on Azure: Azure Data Lake, Azure Data Factory, Blob Storage, Web Apps, COSMOS DB, SQL DB, Azure Funtions, Logic Apps, Key Vaults, Active Directory, Event Hub, App Insights, Azure Data Bricks, Machine Learning Services, Redis Cache, Azure Front Door, Azure API Management, Virtual Machines and Docker Containers.

The client is using Akaimai as web app firewall and also has integration with Unisys Weblynx, so frequent coordination with these parties along with client IT security and client IT infrastructure departments is needed.

Consultant is to perform the following services.

Accountable for the design of advanced analytics ecosystem Provide architectural guidance and support to technology stacks for cloud based data oriented services Drive and implement recommendation (upstream) and its integration to downstream for real-time analytics, operational analysis and metrics/reporting.

Develop architecture solutions to minimize risks and costs to include, but not limited to: (1) non-integrated data architecture across stacks, (2) duplicative solutions and data proliferation and (3) data lifecycle inefficiencies.

Improve architectural framework and process to develop platform solutions for projects.

Develop a conceptual architecture with modernized and service-oriented approach.

Develop a balanced blueprint, maximizing traditional components and emerging strategies of AI.

Evaluate current state (data/platform architectures) and identify incremental opportunities to improve operational efficiencies, delivery throughput and “shift” to modernized architecture methods and components.

Collaborate to define data models and platform security requirements.

Provide senior leadership guidance and direction.

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