Backend Engineer

Cloud infrastructure solutions and support company. (SE1)

Backend Engineer

Multi Recruit

Company

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Backend Engineer

at Cloud infrastructure solutions and support company. (SE1)

Agency job

via Multi Recruit

4 - 8 yrs

₹28L - ₹36L / yr

Bengaluru (Bangalore)

Skills

SQL

Python

R Programming

AI Engineer

Set up

Manage

Automate

Deploy AI Models

Set up, manage, automate and deploy AI models in development and production infrastructure.
Orchestrate life cycle management of AI Models
Create APIs and help business customers put results of the AI models into operations
Develop MVP ML learning models and prototype applications applying known AI models and verify the problem/solution fit
Validate the AI Models
Make model performant (time and space) based on the business needs
Perform statistical analysis and fine-tuning using test results
Train and retrain systems when necessary
Extend existing ML libraries and frameworks
Processing, cleansing, and verifying the integrity of data used for analysis 
Ensuring that algorithms generate accurate user recommendations/insights/outputs
Keep abreast with the latest AI tools relevant to our business domain

Job Qualifications

Bachelor’s or master's degree in Computer Science, Statistics or related field 
A Master’s degree in data analytics, or similar will be advantageous.
3 - 5 years of relevant experience in deploying AI models to production
Understanding of data structures, data modeling, and software architecture
Good knowledge of math, probability, statistics, and algorithms
Ability to write robust code in Python/ R
Proficiency in using query languages, such as SQL
Familiarity with machine learning frameworks such as PyTorch, Tensorflow and libraries such as scikit-learn
Worked with well know machine learning models ( SVM, clustering techniques, forecasting models, Random Forest, etc.)
Having knowledge in CI/CD for the building and hosting the solutions
We don’t expect you to be an expert or an AI researcher, but you must be able to take existing models and best practices and adapt them to our environment.
Adherence to compliance procedures in accordance with regulatory standards, requirements, and policies. 
Ability to work effectively and independently in a fast-paced agile environment with tight deadlines 
A flexible, pragmatic, and collaborative team player with an innate ability to engage with data architects, analysts, and scientists.

Set up, manage, automate and deploy AI models in development and production infrastructure.
Orchestrate life cycle management of AI Models
Create APIs and help business customers put results of the AI models into operations
Develop MVP ML learning models and prototype applications applying known AI models and verify the problem/solution fit
Validate the AI Models
Make model performant (time and space) based on the business needs
Perform statistical analysis and fine-tuning using test results
Train and retrain systems when necessary
Extend existing ML libraries and frameworks
Processing, cleansing, and verifying the integrity of data used for analysis 
Ensuring that algorithms generate accurate user recommendations/insights/outputs
Keep abreast with the latest AI tools relevant to our business domain

Job Qualifications

Bachelor’s or master's degree in Computer Science, Statistics or related field 
A Master’s degree in data analytics, or similar will be advantageous.
3 - 5 years of relevant experience in deploying AI models to production
Understanding of data structures, data modeling, and software architecture
Good knowledge of math, probability, statistics, and algorithms
Ability to write robust code in Python/ R
Proficiency in using query languages, such as SQL
Familiarity with machine learning frameworks such as PyTorch, Tensorflow and libraries such as scikit-learn
Worked with well know machine learning models ( SVM, clustering techniques, forecasting models, Random Forest, etc.)
Having knowledge in CI/CD for the building and hosting the solutions
We don’t expect you to be an expert or an AI researcher, but you must be able to take existing models and best practices and adapt them to our environment.
Adherence to compliance procedures in accordance with regulatory standards, requirements, and policies. 
Ability to work effectively and independently in a fast-paced agile environment with tight deadlines 
A flexible, pragmatic, and collaborative team player with an innate ability to engage with data architects, analysts, and scientists.

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