Software Engineering

Technical Partner | Application Engineering | GTA

Work Type: Full Time

About the role:


As a leader of the application engineering team, you will be  responsible for all the web applications and interfaces that enable  consumption of inhouse machine learning models and insights. You will work  with analytics consultants, data scientists, data engineers, ML engineers,  developers etc. to:


·       Create custom  solutions/applications/demos for a variety of client business problems

·       Integrate our ML engines  with a variety of new/existing client systems. More specifically, the team of  application architects and developers that you lead will:

·       Determine user  needs by analysing technical requirements.

·       Write well  designed, testable, efficient code by using best software development  practices.

·       Develop, validate  test routines and schedules to ensure that test cases mimic external  interfaces and address all browser and device types

·       Implement tools  and create reusable libraries and frameworks.

Required Skills &  Experience:


15+ years of  application engineering experience which includes:

Designing complex applications and  communicating the design with various stakeholder

Building  high-performance applications using parallel/distributed computing pattern

Enterprise  software and security consideration

Building solutions on cloud  environments

Hands-on  experience with most of the below:

  • Excellent knowledge of Python  and (optionally) Java
  • Web technologies like  JavaScript, HTML5, CSS3
  • Web frameworks like Angular,  React, Django
  • Web server technologies like  Flask, Node.js, Apache, Nginx
  • Database technologies  like MySQL, Oracle, PostgreSQL, and MongoDB
  • Configuration management tools  like Puppet, Ansible, Saltstack
  •  Front-end (CSS) frameworks like  Foundation or Bootstrap 
  •  Docker, Kubernetes (good to  have)
  • Setting up and using CI/CD
  • RESTful services, API design and development
  • (Good to have)  Data/Machine learning related exposure which includes
  • Building performant apps for AI/ML models
  • Security/Firewall considerations while deploying ML models
  •  Handling large scale data  (GBs to TBs)


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