AI Head

Designation: AI Head

 

Required skills

  • Extremely strong in Python Programming, Tensor flow, Sci-kit learn, Keras, Theano, and other relevant Python Libraries for AI & Machine Learning (Scikit learn, NLTK, Scipy, etc.). Scale of 8/9 on 10.
  • Strong ability to manipulate data using Pandas/Numpy and other python libraries, Should be able to support all data munging and cleansing activities as part of guiding and leading an AI-platform implementation team
  • Experience in working in Agile methodology in a very high performance coding environment with high level of automation.
  • Strong hands-on skills in relational data bases. Ability to write SQL like queries to create new joins or data frames with a goal of framing the right business questions for the AI platform development team
  • Ability to white board with clients all types of possible solutions end-to-end (front end- API’s and Back end).
  • Good handle on concepts of enterprise application architecture, solution architecture and enterprise architecture

 

Tools & Technologies

  1. Python, TensorFlow, Keras, PyTorch, Django/Flask based API’s, Pandas, Numpy, Matplotlib, Seaborn, etc.
  2. Databases: MySQL or any enterprise RDBMS
  3. Code Management: Git, Bitbucket, Jira, Confluence, etc.
  4. Operating System: Mac, Linux, Windows
  5. Cloud Technologies: comfortable with Google Cloud, AWS and Azure

 

Key Responsibilities

  • Ideate and formulate a pragmatic blueprint for enterprise AI platform solution and subsequent development using Deep learning with a cross-functional team of talented engineers, Motivated self-learner
  • Collaborate well with engineers, researchers, and data implementation specialists to design and create advanced, elegant and efficient AI based solutions for business problems
  • Provide real time knowledge transfer to the team on the Requirements based on client facing meetings and white boarding workshops
  • Help in writing a comprehensive proposal with the help of the other engineers on the project
  • Work in an agile environment based on the defined sprint backlogs to deliver the assigned work in the stipulated timelines.
  • Adhere to software development best practices and coding standards in all work products and participate in the refinement of those practices and standards to improve quality and productivity.

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