Job Title: Python Software Engineer (with ML/AI Exposure)

Overview:

We are looking for a talented Python Software Engineer with 4–5 years of professional

experience building scalable applications, APIs, and data-driven systems. The ideal candidate

has strong Python fundamentals, experience with backend services and data processing, and

some exposure to machine learning workflows. This role focuses primarily on Python

engineering, with opportunities to contribute to AI/ML features as needed.

Key Responsibilities :

• Design, develop, and maintain Python-based services, applications, and automation

tools.

• Build and optimize REST APIs, microservices, and backend systems.

• Develop and maintain ETL/data processing pipelines for analytics or ML teams.

• Collaborate with product, data, and engineering teams to deliver reliable and

maintainable software.

• Write clear, well-tested, and scalable Python code with strong attention to architecture

and quality.

• Assist with operational aspects such as logging, monitoring, performance tuning, and

deployment.

• Support light AI/ML integration tasks (e.g., preparing data, calling model endpoints,

packaging model-serving code).

• Document new features, system behavior, and operational procedures.

Required Skills & Qualifications:

• 4–5 years professional experience in software engineering with a focus on Python.

• Strong command of Python fundamentals, including async programming, OOP, and

design patterns.

• Experience building and maintaining APIs and backend services (FastAPI, Flask,

Django, etc.).

• Working knowledge of Git, CI/CD workflows, testing frameworks (PyTest, unittest), and

code quality practices.

• Experience with cloud platforms (AWS, Azure) for deploying Python applications.

• Familiarity with containerization (Docker) and orchestration basics (Kubernetes is a

plus).

• Ability to collaborate in an agile environment and communicate clearly with cross

functional teams.

Preferred Qualifications

Experience with data pipelines, message queues, or streaming systems (Airflow, Kafka,

Celery).

• Basic understanding of machine learning concepts and ability to integrate ML models

into production services (no heavy ML experience required).

• Experience with caching layers (Redis, Memcached).

• Experience with monitoring tools like CloudWatch or datadog