Python Software Engineer (with ML/AI Exposure)

Overview

Python Software Engineer, AI/ML, REST APIs, ETL, Flask, Django, cloud platforms, Kafka,

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