ML Inference Systems Engineer · Istanbul, Turkey · open to collaborations
Building production ML inference systems. Google Developer Expert, Cloud AI · Google Dev Group organizer · Speaker · OSS contributor
A queueing model of my day job: requests get dynamically batched onto MIG slices of one H100. Send a burst, then drop max batch to 1 and watch p99. Service times are modeled, not measured.
I work on ML inference systems: deploying and optimizing the platforms that serve recommendation engines, search ranking, and increasingly AI agents in production. Most recently that meant a platform serving 100M+ users at over 1M requests per minute.
My work sits at the intersection of ML, systems, and infrastructure — vLLM and Triton for serving, and Kubernetes across cloud and on-prem. I care about the parts of ML that don't make it into most conference talks: p99 latency, GPU utilization, what breaks at 3am.
I'm a Google Developer Expert in Cloud AI. I co-organize GDG Istanbul and speak regularly at DevFest, Build with AI, and GDG events across Turkey and abroad, in both Turkish and English.
These are the real attention weights of GPT-2 small reading the three paragraphs above, pooled from tokens to words. GPT-2 only looks backwards, so arcs always point to earlier words.
Each square is one head; rows are layers 0–11, and brighter squares look further back. Click one to switch.
computed by scripts/ml_figures.py · <|endoftext|> sink removed, rows renormalized
Turkcell
2022 – 2024
A team of research, judge and builder agents talking over A2A, with a self-hosted Gemma model on Cloud Run GPUs.
A recorded run of my issue-to-PR agents, replayed with its real timestamps, tokens and cost. All recorded runs →
Planner, coder and reviewer agents in an ADK graph, routed by real diffs and test results instead of model claims. Commands run in sandboxes with no credentials or network, every run is cost-capped, and a human approves before a pull request opens. Deployed on Agent Runtime with keyless CI/CD. Watch recorded runs · architecture
Four 4-bit models under AIPerf at 1 to 16 concurrent users. Bursty traffic made time-to-first-token about 5x worse than steady traffic at the same average rate, and the p99 knee came early. Scripts and raw per-request data included.
Serve a ResNet-18 with dynamic batching and two model instances, load test it with perf_analyzer, and deploy it to GKE. Runs on a laptop CPU. Companion code for my intro article.
Collects new AI papers and posts every day, ranks them by what you like (with or without an LLM), and lets you read full papers without leaving the terminal. Includes hybrid search and Q&A over everything you've read.
A Claude Code plugin (goygoy): hand Claude a task and it answers "Başımla beraber abi! 🫡" — out loud, or in its own reply.
I'm open to collaborations: talks and workshops, open-source work, and teams figuring out how to serve models or agents in production. Or just write to compare notes on inference at scale.
