Architecture and infrastructure for AI platforms

When your company needs this

Your product must receive data, prepare versions, run models and deliver responses through an application programming interface (API). Separate scripts and manual tasks make it hard to track versions, usage and recovery points.

The consulting work organises this path as a platform. I work with your team to define what the company must control and what can use an external service.

Implementation experience

• Dataset preparation with stable group assignment across training, validation and test sets. Each group stays in one set to avoid evaluating on examples related to training data.
• Collection with content comparison, batch writes and updates for changed records. Documents are stored in ClickHouse and projected into MySQL search, with recovery from the preserved source.
• Distributed training with usage limits, temporary task ownership and recovery points. Each assignment has an execution version that rejects updates from a previous worker.
• File, size, hash and label checks before registering a trained model. A verification failure allows that step to run again without repeating training.
• An OpenAI-compatible inference API with model access scoped by organisation and key. Responses stream to the client, while usage and latency records are kept separate from model input.

What your team can engage me for

• Data flow architecture, quality criteria and records of the versions used in each run.
• Training and inference worker integration with budgets, permissions and recovery.
• API deployment, ongoing operations and documentation for the team that runs the service.

How to define the scope

These examples describe engineering experience without attributing outcomes to clients. The proposal defines which parts will be deployed in your environment and how they will be verified.

Model training is not a required step. I first assess the task, data and integration options. Capacity, cost and quality must be measured with your company's workload.