Engineering Head - Python
Engineering Head - Python
Posted on :
14-07-2025
Employer Active
1 Vacancy
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Job Description
Responsibilities
Application and Back-End Development:
Design implement and maintain back-end systems and APIs using Python
frameworks such as Django Flask or FastAPI focusing on scalability
security and efficiency.
Build and integrate scalable RESTful APIs ensuring seamless interaction
between front-end systems and back-end services.
Write modular reusable and testable code following Pythons PEP 8 coding
standards and industry best practices.
Develop and optimize robust database schemas for relational and non-
relational databases (e.g. PostgreSQL MySQL MongoDB) ensuring efficient
data storage and retrieval.
Leverage cloud platforms like AWS Azure or Google Cloud for deploying
scalable back-end solutions.
Implement caching mechanisms using tools like Redis or Memcached to
optimize performance and reduce latency.
AI/ML Development:
Build train and deploy machine learning (ML) models for real-world
applications such as predictive analytics anomaly detection natural
language processing (NLP) recommendation systems and computer vision.
Work with popular machine learning and AI libraries/frameworks including
TensorFlow PyTorch Keras and scikit-learn to design custom models
tailored to business needs.
Process clean and analyze large datasets using Python tools such as
Pandas NumPy and PySpark to enable efficient data preparation and feature
engineering.
Application and Back-End Development:
Design implement and maintain back-end systems and APIs using Python
frameworks such as Django Flask or FastAPI focusing on scalability
security and efficiency.
Build and integrate scalable RESTful APIs ensuring seamless interaction
between front-end systems and back-end services.
Write modular reusable and testable code following Pythons PEP 8 coding
standards and industry best practices.
Develop and optimize robust database schemas for relational and non-
relational databases (e.g. PostgreSQL MySQL MongoDB) ensuring efficient
data storage and retrieval.
Leverage cloud platforms like AWS Azure or Google Cloud for deploying
scalable back-end solutions.
Implement caching mechanisms using tools like Redis or Memcached to
optimize performance and reduce latency.
AI/ML Development:
Build train and deploy machine learning (ML) models for real-world
applications such as predictive analytics anomaly detection natural
language processing (NLP) recommendation systems and computer vision.
Work with popular machine learning and AI libraries/frameworks including
TensorFlow PyTorch Keras and scikit-learn to design custom models
tailored to business needs.
Process clean and analyze large datasets using Python tools such as
Pandas NumPy and PySpark to enable efficient data preparation and feature
engineering.
Employment Type
Full-time
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