Location: On-site

Schedule: Full-Time

Reports to: ChiefEngineer

About the Role:

We are seeking a skilled and independent MachineLearning Programmer to provide crucial support for our engineering team. Youwill be the key technical resource for implementing customer-drivenmodifications and troubleshooting our specialized systems. The core platforminvolves Python-based applications running on Raspberry Pi devices. You willwork under the guidance of our Lead Engineer but must be proactive inunderstanding our existing codebase and capable of delivering tailoredsolutions directly impacting customer satisfaction.

Key Responsibilities:

1. Collaborate with the Chief Engineer to analyze, estimate, and implement customer feature requests andmodifications.

2. Customize, debug, and enhance existing Python-based systems on Raspberry Pi according to specific customer requirements (e.g., adjusting video playback speed, modifying UI elements likefont size, customizing data output formats).

3. Independently navigate, comprehend, and modify the existing source code to implement changesefficiently and without regressions.

4. Provide ad-hoc debugging support to resolve system issues reported by customers or the engineering team.

5. Maintain clear documentation for code changes and configurations.

6. Participate in occasional knowledge transfer sessions, which may require travel to client offices orclinics for on-site support and training.

Required Skills & Qualifications:

1. Proven experience in Python programming, with a strong grasp of software development principles.

2. Hands-on expertise in developing, deploying, and troubleshooting software on Raspberry Pi platforms.

3. Ability to quickly understand and work with an existing, mature codebase with minimal supervision.

4. Strong problem-solving skills and a detail-oriented approach to debugging.

5. Excellent communication skills to interact effectively with the engineering lead and document work clearly.

6. Self-motivated, reliable, and able to manage a flexible, ad-hoc workload with a high degree of independence.