Technical Skills Priority 2: AWS AI service, AWS ML service
Technical Skills Priority 3: Exposure to ML Ops offerings
No of Years of Experience: 57 years
Preferred Industry Experience: Wealth and Insurance
Educational Qualifications: Background in Maths & Statistics
Location: Remote
Contract Duration: 6 months
Mobilization Period: 2 to 3 weeks
Job Description
You must have deep technical experience working with technologies related to artificial intelligence, machine learning and deep learning. A strong mathematics and statistics background is preferred, in addition to experience building complex classificationextraction models extracting data from documents i.e. form, unstructured documents and IDs. You will be familiar with the ecosystem of consulting partners and software vendors in the AIML space, and will leverage this knowledge to help Intellect customers in their selection process.
Technical skills:
AI architecture and pipeline planning. Understand the workflow and pipeline architectures of ML and deep learning workloads. An indepth knowledge of components and architectural tradeoffs involved across the data management, governance, model building, deployment and production workflows of AI is a must.
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, Handson experience to Build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows
AWS ML services, Handson experience to Build, train, deploy, monitor and govern machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows
MLOps principles. Exposure to AWS MLOps offerings
Nontechnical skills include:
Wealth Insurance Domain Knowledge is advantageous.
Thought leadership. Be change agents to help the organization adopt an AIdriven mindset. Take a pragmatic approach to the limitations and risks of AI, and project a realistic picture in front of executivesproduct owners who provide overall digital thought leadership.
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 (technical and nontechnical personnel), and harmonize the relationships among them.
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