I built data platforms, now LLM platforms: gateways, inference routing, serving on Kubernetes, agents on top.
atiklhy.fr · writing · linkedin · email
- Ondine: batch LLM processing for tabular data. Checkpointing, budgets, 100+ providers.
- llm-d-router and llm-d: inference scheduling on Kubernetes
- AIBrix: LLM gateway
- Narwhals, Great Expectations, Cognee, OpenLineage: data and agent memory
- llm-d-router#3379: a pod that had never served a request got no traffic, so it never reported, so it stayed idle. Reproduced on a bench, then confirmed by the reporter on 4 x H100: idle pod in 10 of 12 runs before, 0 of 12 after.
- Data: where I come from. Spark, Kafka, Databricks, dbt, Polars, and the Hadoop era before that.
- Inference: vLLM, SGLang, LiteLLM, Kubernetes. Queueing theory when the numbers stop making sense.
- Agents: LangGraph, MCP, evals and tracing. Also Vauban, a reference LLM platform and agent lab I am building as a course: 65 controls, every claim tagged measured or gap. Private for now.
Python, SQL, Go.



