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1 week ago
You must have deep technical experience working with technologies related to artificial intelligence, machine learning and deep learning. A strong mathematical foundation is required, in addition to experience building complex classification/extraction models extracting data from documents i.e. form, unstructured documents and IDs.
Technical skills:
- Ai Architecture planning. Understand the workflow and pipeline architectures of ML and deep learning workloads.
- Data science and advanced analytics , including knowledge of advanced analytics tools (such as Python) along with applied mathematics, ML and Deep Learning frameworks (such as PyTorch, Tensorflow) and ML techniques (such as neural networks, transformers and graphs).
- AWS AI services, Hands on experience to Build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows
- MLOps principles Exposure to AWS MLOps offerings
Non-technical skills include:
- Wealth / Insurance Domain Knowledge is advantageous.
- Thought leadership Be change agents to help the organization adopt an Ai-driven mindset.
- Collaborative mindset To ensure that Ai platforms deliver both business and technical requirements, seek to collaborate effectively with data scientists, data engineers, data analysts, ML engineers, other architects, business unit leaders and CxOs.
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