AI development and chatbots · delivered in 3-10 days
Senior-level ML and data science work, taken all the way from raw data to a live, production-ready system on Google Cloud Platform or Hugging Face, drawing on 15 years of engineering and technical leadership experience.
This is the senior tier of the ML and data science service, for work that needs deeper expertise, more complex architecture and a full production deployment rather than a single model or script.
Every engagement is built and shipped end-to-end, from raw data through to a live, user-facing system. With 15 years of engineering and technical leadership behind the work, the focus stays on solving a specific operational problem rather than demonstrating a technique for its own sake.
The service covers the breadth of ML and data science work: predictive modelling for forecasting and decision support, computer vision built on deep learning and convolutional networks, time-series forecasting for demand and operational planning, process automation and internal tooling, and full deployment pipelines with continuous integration on Google Cloud Platform and Hugging Face.
Delivered systems span a wide range of uses: anomaly detection from audio signals for predictive maintenance, applications that forecast outcomes from environmental and behavioural data, demand forecasting for inventory planning, image classification systems with a live front end, and automated tools that streamline manual review and grading work.
The stack in use includes Python, TensorFlow, Scikit-learn, MLflow, Hugging Face, Docker and Google Cloud Platform, alongside deep experience in data pipelines, preprocessing, feature engineering and workflow automation using tools such as Make and n8n.
This is a premium-rate, senior service. Message before ordering so the project can be scoped properly, the deliverables agreed and the right number of hours worked out — then select that number of hours as your quantity at checkout.
Your order opens its own thread here the moment it is paid, and everything about that order - questions, changes and the final report - happens in it.
Senior-level projects are complex and every one is different. Understanding your data, your business goals, the technical constraints and the outcome you expect is what makes it possible to scope the work properly. That conversation protects both sides: you get an accurate estimate and clear deliverables, and it confirms this is the right fit before any payment is made.
Each package represents a fixed block of senior time. Pick the one nearest the size of your project, then adjust the quantity at checkout if more hours are needed — for instance, ordering the smallest block three times over covers a larger job. The exact scope and any milestones are settled before the order goes ahead.
You will be told well before the agreed hours run out. From there the choice is to extend the scope or reprioritise what gets delivered within the hours already agreed. Nothing is billed beyond what you have explicitly agreed to.
Both are hands-on engineering — writing code, building models and deploying systems either way. The difference is depth: this senior tier is for advanced architectures, multi-component system design, full continuous-integration pipeline setup and production-grade deployment with full documentation. The standard service suits a well-defined, focused task such as training one model, preparing data, or a straightforward deployment. If you are not sure which fits, send a message and the right one can be recommended.
Yes. You supply the dataset or data source. During scoping, guidance is given on data format, volume and quality expectations, and any preparation that should happen before work begins.
Deployment happens mainly on Google Cloud Platform and Hugging Face, with continuous-integration pipelines set up using Docker, GitHub Actions and MLflow so the deployed system stays maintainable, scalable and easy to update after handover.
Yes. All source code, trained models, pipeline configuration and documentation belong to you, so your own team can maintain, retrain and extend the system independently once the project is complete.
Yes. Picking up a project that has stalled, hit a technical wall, or needs senior-level work to reach production is common. Share whatever exists — code, notebooks, documentation — during scoping, and the build continues from where things stand rather than starting again from scratch.
A typical setup includes automated retraining triggers, containerised deployment with Docker, continuous integration through GitHub Actions, experiment tracking and model versioning through MLflow, and deployment scripts for Google Cloud Platform or Hugging Face. The exact setup depends on the project and is defined during scoping.
Yes, every package comes with documentation matched to the work delivered. The larger package includes a full written record of the system's architecture, the data pipeline, how the model performs, how it was deployed and how to maintain it — enough for another developer or your own team to pick up and manage the system later.
No. Every package is hands-on engineering. Even the smallest, single-hour block is real development time — writing code, building pipeline components, evaluating models, debugging or solving a technical problem. It is an hour of senior-level work actually getting done, which suits a small focused task, an urgent fix, or making real progress on part of a bigger project.
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