Job Description

Job Title IT Software

Experience

School Degree

Description

The AI/ML Lead will report directly to the Chief Executive Officer and Head of the GIC. She or He will work closely with other executives within firm as well as the broader team on all aspects of strategic planning, execution, product delivery, as well as providing close support to the Head in managing priorities, risks, and external client, stakeholder, and partner support.
Working closely with the COO of GIC for all deliverable tracking functions in the AI/ML areas: Tracking and Reporting processes covering KPIs, including deliverables and project/program updates, budget, and risks/mitigations for use by the CEO and other governance bodies
Translating the executive management requirements for all key roles to be filled, into detailed job descriptions and technical requirements
Management of the recruitment pipeline, including details of preliminary vetting of the candidates together with such partners as recruitment agencies and search firms
Facilitating technology landscape research, and supporting the development of AI/ML expertise within the local ecosystem
Taking ownership for defining and implementing the full Agile process, tools, and protocols, including documentation processes, as well as the product standards to be followed within the GIC for all project and program work within their remit, using modern tools, team structures, and technology
Until such time as an independent architecture function is installed, this role will also ensure that an architecture discipline and standards are established and documented to support the work of the engineers and product professionals at the center, in collaboration with the CEO and COO.
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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