Ongoing AI/ML Engineering Partnership
Budget: €1,500 – €3,000 EUR
I have a growing pipeline of AI-driven products that need to move from idea to production over the coming year. Every project centres on developing brand-new applications, so I’m looking for engineers who can own the full lifecycle: turning raw data into a reliable, cloud-hosted model and keeping it healthy in the wild.
The work spans three core domains—Natural Language Processing, Computer Vision, and Predictive Analytics—and typically involves:
• Data preprocessing and cleaning
• Model development and training (Python, TensorFlow or PyTorch preferred)
• Containerised deployment and continuous monitoring (Docker/Kubernetes, AWS or GCP)
You’ll step into projects such as conversational agents, visual quality-inspection tools, and forecasting engines, collaborating with me on architecture choices, experiment design, and MLOps best practices. Clean, well-documented code and reproducible experiments are non-negotiable; I want to be able to pick up any model six months later and understand exactly how it was built.
Deliverables for each assignment will generally include:
1. A version-controlled repo with preprocessing pipelines, training scripts, and inference code
2. A reproducible environment file or Docker image
3. Deployed endpoint or service with basic monitoring hooks
4. A short hand-off report outlining results, limitations, and next steps
If you’re interested in a long-term collaboration where you can tackle diverse, green-field challenges, tell me about one recent NLP, CV, or forecasting project you took from dataset to deployment and the specific problems you solved along the way.
The work spans three core domains—Natural Language Processing, Computer Vision, and Predictive Analytics—and typically involves:
• Data preprocessing and cleaning
• Model development and training (Python, TensorFlow or PyTorch preferred)
• Containerised deployment and continuous monitoring (Docker/Kubernetes, AWS or GCP)
You’ll step into projects such as conversational agents, visual quality-inspection tools, and forecasting engines, collaborating with me on architecture choices, experiment design, and MLOps best practices. Clean, well-documented code and reproducible experiments are non-negotiable; I want to be able to pick up any model six months later and understand exactly how it was built.
Deliverables for each assignment will generally include:
1. A version-controlled repo with preprocessing pipelines, training scripts, and inference code
2. A reproducible environment file or Docker image
3. Deployed endpoint or service with basic monitoring hooks
4. A short hand-off report outlining results, limitations, and next steps
If you’re interested in a long-term collaboration where you can tackle diverse, green-field challenges, tell me about one recent NLP, CV, or forecasting project you took from dataset to deployment and the specific problems you solved along the way.