Senior Python & GenAI Developer
Budget: $25 – $50 USD
I’m looking for a seasoned Python–SQL engineer (about 6 + years hands-on, with at least two years applying Generative AI in production) to jump straight into an active codebase. Everything happens in VS Code with GitHub Copilot, so you’ll need to be fully comfortable letting Copilot speed you up while still exercising strong code-review discipline.
Day-to-day work
• Code new features across several micro-services written in Python.
• Optimise existing modules for speed, memory and readability.
• Perform data analysis to validate feature outcomes and uncover new insights.
• Write and tune complex queries, views and procedures in our primary SQL Server database (experience with MySQL or PostgreSQL is a plus, but SQL Server mastery is essential).
Generative AI scope
You will own the full model lifecycle: training, fine-tuning and ultimately deploying our Gen AI models into a live environment. If you’ve worked with frameworks such as Hugging Face Transformers, LangChain or similar—and can back it up with examples—that will help you ramp up quickly.
What success looks like
1. Clean, well-documented Python and SQL code merged through pull requests with meaningful reviews.
2. Noticeable performance gains in legacy modules, demonstrated by benchmarks we’ll agree on up front.
3. At least one Gen AI model trained, fine-tuned and running in production behind an API, with reproducible training notebooks and deployment scripts.
If this matches your skill set and you can start soon, let’s talk.
Day-to-day work
• Code new features across several micro-services written in Python.
• Optimise existing modules for speed, memory and readability.
• Perform data analysis to validate feature outcomes and uncover new insights.
• Write and tune complex queries, views and procedures in our primary SQL Server database (experience with MySQL or PostgreSQL is a plus, but SQL Server mastery is essential).
Generative AI scope
You will own the full model lifecycle: training, fine-tuning and ultimately deploying our Gen AI models into a live environment. If you’ve worked with frameworks such as Hugging Face Transformers, LangChain or similar—and can back it up with examples—that will help you ramp up quickly.
What success looks like
1. Clean, well-documented Python and SQL code merged through pull requests with meaningful reviews.
2. Noticeable performance gains in legacy modules, demonstrated by benchmarks we’ll agree on up front.
3. At least one Gen AI model trained, fine-tuned and running in production behind an API, with reproducible training notebooks and deployment scripts.
If this matches your skill set and you can start soon, let’s talk.
Related categories:
Python
SQL
Software Architecture
MySQL
PostgreSQL
Data Analysis
Microservices
Generative AI