Python Application Developer

Important Notes from Hiring Manager
Primarily looking for candidates with a strong Statistical background.
PhD candidates with Statistics-focused academic/research experience are highly preferred.
Candidates with a master's degree and strong Statistical Analysis expertise will also be considered.
Python development experience remains mandatory.
Digital Signal Processing (DSP) knowledge is highly desirable.
Job Description
Key Responsibilities:
Design, develop, and maintain Python-based applications using frameworks such as Django, Flask, or FastAPI.
Good knowledge of digital signal processing
Build RESTful APIs and integrate them with front-end and data systems.
Collaborate with cross-functional teams to define, design, and deliver new features.
Write clean, efficient, and reusable code following Python and PEP 8 best practices.
Implement testing (unit, integration, and API) and maintain code reliability.
Work on database design, optimization, and integration (SQL or NoSQL systems).
Debug, deploy, and monitor applications using version control and CI/CD pipelines.
Collaborate with DevOps for containerization (Docker) and cloud deployment (AWS/Azure/GCP).
Ensure application security, scalability, and performance optimization.

Required Skills and Qualifications:
BS or MS in ECE or CS from a prestigious university in the US.
4–8 years of hands-on experience in Python application development.
Proficient in frameworks such as Django, Flask, or Fast API.
Strong understanding of object-oriented programming (OOP) and design patterns.
Experience with RESTful API development and third-party integrations.
Knowledge of SQL/NoSQL databases (PostgreSQL, MySQL, MongoDB, etc.).
Familiarity with front-end basics (HTML, CSS, JavaScript) for integration.
Exposure to containerization and DevOps tools (Docker, Kubernetes, Jenkins).
Understanding of Git version control, testing frameworks (pytest/unittest), and agile delivery processes.
Preferred/Good-to-Have Skills
Experience with data processing (Pandas, NumPy) or machine learning integration.
Knowledge of microservices architecture.
Exposure to cloud technologies and CI/CD automation.
Experience working with APIs, security standards, and performance tuning.