Job Role

• 2+ years of experience working with Python, including strong knowledge of frameworks like Django or Flask and experience with version control systems such as Git.

• Develop, test, and maintain Python-based applications and scripts, ensuring clean, scalable and reusable code following best practices.

• Design and implement RESTful APIs to support web and mobile applications. Contribute to all phases of the software development lifecycle, from planning and design to deployment and maintenance.

• Perform thorough code reviews, contribute to technical documentation and stay updated on the latest Python libraries, frameworks, and development practices.

• Collaborate with product managers, designers, and developers to implement efficient software solutions while debugging and resolving technical issues to improve performance. Work with both relational and non-relational databases (e.g., SQL, NoSQL) for efficient data storage and retrieval.

• Keep abreast of trends and technologies in AI and machine learning, with a basic knowledge of concepts such as supervised learning, regression, or classification, and a willingness to actively contribute to AI-related projects and integrate new technologies where relevant.

• Proficient in database systems like MySQL, PostgreSQL, MongoDB, with a solid understanding of object-oriented programming (OOP) and software design patterns.

• Familiarity with front-end technologies like HTML, CSS, and JavaScript is a plus, as well as experience with RESTful API design and development.

• Ability to write unit and integration tests, paired with excellent problem-solving and debugging skills. Strong communication skills with the ability to work effectively in a collaborative environment.

• Experience with cloud platforms such as AWS, Google Cloud, or Azure, along with familiarity with Docker and Kubernetes for containerization and orchestration. Additionally, possess experience in asynchronous programming (e.g., using asyncio, Celery) and exposure to machine learning or data science libraries (e.g., pandas, NumPy, scikit-learn), along with a basic understanding of machine learning models and their lifecycle.