AI/ML Engineer (AVP)

Experience: 6 to 10 years
Location: Abu Dhabi
Job code: 100462
Posted on: Apr 28, 2022
Job Description:

The successful candidate would have direct, relevant experience in AI/ML product/solutions/ systems design, build, and deployment experience in global settings. The candidate would have demonstrated excellence in managing highly skilled and trained staff delivering AI Product, and excellence in managing high quality product delivery practices. In addition to these technical skills, the candidate would need to demonstrate leadership, teamwork, client alignment, and industry acumen throughout their career history.
Playing a key role in implementing the Agile process, tools, and protocols, including documentation processes, as well as product standards within the GIC for all project and program work within their remit, using modern tools, team structures, and technology
· Taking responsibility as required for interfacing with 3rd Party service providers within the AI/ML department scope, including external 3rd party data and technology services.
· Taking responsibility as required for the project management, Devops, Quality management, and release management functions for all products and platforms built and released by the center, in the specific areas within the

Technology Stack:
Languages and Packages: SQL, Python, Conda Package Manager, R, R*Shiny, scikit-learn, Spark, JavaScript, Node.JS, Neo4J Cypher
· Cloud Services: AWS (Sagemaker, Lake Formation, S3, EC2, etc.), Google Cloud, Azure Services, AWS Services – S3, AWS SageMaker
· Databases: MySQL, MS SQL, Oracle, Neo4J
· Data Automation: Lake Formation, Palantir Foundry, Spark
· Data Analytics Tools: Power BI, Palantir Foundry, HubSpot, Google Analytics
· Tools and Environments: JIRA, Bitbucket, Confluence, Palantir Foundry, Jupyter Notebook, MLFlow

Tech Practice and Expertise:
· Understanding of process for Algo and Model Prototyping, Construction, and Validation using Agile approach
· Understanding of Supervised and Unsupervised ML paradigms
· Exposure to Core Frameworks (Tensorflow, Caffe, PyTorch, SparkMLlib, etc.)
· Exposure AI ML Platform Services including AutoML (AWS Sagemaker, AutoPilot)

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