I build the backends and annotation systems behind enterprise AI data programs. I also write the architecture docs that get them approved.
I'm a Technical Lead with 10 years of experience shipping production software. I started in enterprise .NET and Angular. These days it's mostly Python backends for AI data work: media processing, annotation platforms, and wiring model calls into delivery workflows.
My week splits in two. Half is backend code in Python and FastAPI, with the validation, review and delivery pipelines that run on Azure Kubernetes Service. The other half is scoping: taking a client's requirements and turning them into an architecture, a decision about what we can reuse, a capacity estimate, and a spec an engineering team can pick up without a meeting.
The interesting part is rarely the model. It's the plumbing around it. Getting data in, keeping quality honest, and making the thing observable enough that you notice when it breaks instead of hearing about it from the client.
Services that take large batches of media through ingest, validation, review and delivery. Multi-stage pipelines, background workers, retries that don't lose files, and the dashboards delivery teams live in.
Annotation projects built on a Label Studio-based platform: labeling configs, custom JS plugins, task building and export. I write the annotator guidelines too, which is what actually keeps quality consistent across text, conversation, video and image work.
Requirements, architecture, what we can reuse from existing services, and what it will cost to run. Then the deployment side of it: containers, Kubernetes, CI/CD and infrastructure as code.
Each weekday I post one AI or engineering concept: what it is, why it matters, and something you can try in five minutes. The archive is searchable, newest first.