Python ML Engineer for Testing

# Hiring: Python / Machine Learning Engineer – Code Development, Testing & Sandbox Experience

**Job Type:** Freelance / Part-time
**Location:** India (Remote)
**Experience:** 1–3 years preferred
**Budget:** Negotiable based on experience

### Project Overview
We are looking for a skilled Python / Machine Learning Engineer with strong software development and problem-solving skills to assist with independent engineering, code development, debugging, testing and evaluation tasks.

The work involves understanding existing codebases, implementing solutions, identifying bugs, running tests and validating code in sandbox environments.

### Responsibilities
- Develop and modify Python-based applications and scripts.
- Work with existing GitHub repositories and understand codebases.
- Debug code and resolve software-related issues.
- Write and execute unit tests and integration tests.
- Work with sandbox environments to run and validate code.
- Evaluate code outputs and identify potential errors.
- Document technical findings and implementation details.
- Work with machine learning and AI-related tools where required.

### Required Skills
- Strong Python programming skills.
- Knowledge of Machine Learning fundamentals.
- Experience with Git and GitHub.
- Familiarity with Linux / command-line environments.
- Experience with debugging and software testing.
- Understanding of Docker and sandbox environments.
- Ability to understand existing codebases and technical documentation.

### Preferred Skills
- PyTorch / TensorFlow / Scikit-learn.
- Software engineering and code review experience.
- Experience with automated testing.
- Familiarity with AI model evaluation workflows.

### Application Requirements
Please share:
1. Your GitHub profile or portfolio.
2. Previous Python / ML projects.
3. Your experience with Docker and sandbox environments.
4. Your hourly or fixed project charges.
5. Your availability.

**Note:** This is independent engineering and technical assistance work. All work must be original, authorized and properly documented. No sharing of confidential project data or credentials.