remote
Python Application Developer - SNS System
Software Engineer
Develop and maintain high‑performance web applications and AI/ML solutions using Python, leveraging frameworks such as Django, Flask, and FastAPI, and implementing models with TensorFlow while managing data in PostgreSQL.
About the role
This is a remote position.
Requirements
- Develop Python-Based Applications: Build and maintain robust web applications using Django, Flask, or Fast API, ensuring scalable and high-performance systems.
- Implement AI/ML Models: Work on exciting AI/ML projects, implementing models using TensorFlow or PyTorch to deliver intelligent solutions that drive business value.
- Work with Databases: Integrate and manage databases such as PostgreSQL, MongoDB, or MySQL to ensure efficient data storage, retrieval, and security.
- Write Scalable and Reusable Code: Ensure that the code you write is clean, efficient, reusable, and scalable, with a focus on long-term maintainability.
- Collaborate on Projects: Work closely with cross-functional teams, including data scientists, designers, and project managers, to ensure smooth development and deployment of AI/ML and web applications.
- Optimize Performance: Continuously optimize the performance of applications, focusing on speed, reliability, and scalability across various platforms and environments.
- Strong Python Programming Skills: Extensive experience with Python and its libraries, including expertise in writing efficient, clean, and maintainable code.
- Experience with Web Frameworks: Solid experience with Django, Flask, or Fast API for developing backend web applications and APIs.
- Knowledge of AI/ML Frameworks: Experience implementing AI/ML models using TensorFlow or PyTorch to build intelligent systems.
- Database Management: Hands-on experience working with databases like PostgreSQL, MongoDB, or MySQL, with a solid understanding of database architecture and optimization.
- RESTful API Development: Familiarity with RESTful APIs and their integration within Python-based applications.
- Cloud Platform Experience: Understanding of cloud platforms (AWS, GCP, Azure) and their use for deploying scalable applications.
- Version Control Knowledge: Experience using version control systems such as Git for collaboration and code management.
- Experience with Docker or containerization.
- Familiarity with DevOps practices and CI/CD pipelines.
- Knowledge of machine learning deployment strategies and model optimization.
Originally posted on Himalayas