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 Python’s 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.
experience
12show more 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 Python’s 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.
experience
12show more
