With the compute and storage cost going down, we see an emergence of AI into the mainstream industry. Increasingly, AI technologies are being explored as a solution to many complex and undeterministic problems in the industry. Application of the right AI model with the right appreciation for data collection and data wrangling is a true definition of Applied AI.

The course covers the pre-requisites in Mathematics, Programming, Data stores and appreciation to Machine and Deep learning. The course will ensure a deep dive into aspects of machine learning, neural networks, applied AI and provides a hands-on experience using classroom assignments, labs, practice examples, exploratory research topics and capstone projects.

JD for Engineer-Trainee – Applied AI Engineering

Experience: 0 years with 6 months of customized training course completion at VEDA IIT

JOB DESCRIPTION for Applied AI Engineer at ATAI

ATAI is looking for individuals responsible for building production class deep learning models
applied to computer vision problems and who are effective team players with the ability to work in
high-performance teams into a fast-paced environment

ROLES AND RESPONSIBILITIES

• Development of end-to-end machine learning pipeline including data extraction, automated
  labelling, training of deep learning model and deployment
• proficient in image processing techniques using OpenCV
• Familiarity in at least one of TensorFlow and Keras, PyTorch, Caffe, MXNet
• Proficient in Python language
• Proficiency with fundamental front-end languages such as HTML, CSS, and JavaScript.
• Proficiency with server-side languages such as Python or Java.
• Familiarity with any SQL & No-SQL database technologies such as
  MySQL/PostgreSQL/Oracle/MongoDB.
• Familiarity with any relevant and globally supported frameworks for both front-end and
  back-end, if necessary. E.g: Django/Flask/Spring MVC/Angular/React
• Strong foundation in software engineering; proficient in design patterns, code optimization
  and debugging
• Experience in Linux environment and DevOps practices/tools
• Working knowledge of statistics relevant to Deep Learning
• Desire to work in a highly collaborative, innovative, small Engineering team
• Strong initiative, self-starter, quick learner, proactive contributor

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