CV · Enes Uzun

15,000employees use the on-prem LLM chatbot
250developers on self-hosted coding models
250+camera streams analysed in real time
8years in production ML

Summary

AI infrastructure engineer at Ford Otosan (Ford Motor Company joint venture) with 8 years of production ML work. I run the on-prem GPU cluster and LLM serving stack used by 15,000 employees and 250 developers. Before that I built real-time computer vision systems in C/C++ that run in four car plants. My recent open-source work is on the reliability of coding agents with self-hosted models.

Experience

Senior Software Engineer

Dec 2022 – Present

Ford Otosan (Ford Motor Company joint venture), Türkiye

Designed and built the company's LLM gateway. It decides per request whether the data may leave the company: confidential code and documents go to open models on our own GPUs, the rest to Azure OpenAI. Access goes through Entra ID (OAuth2/OIDC).
Deployed vLLM on on-prem GPUs behind OpenAI-compatible endpoints, so 250 developers use Copilot CLI with internal models (600+ agent requests a day) without code leaving the network or per-token API costs.
Run the multi-site Kubernetes GPU cluster (5 servers; 2× H200, 4× A100, 4× L40S, 4× V100) with Prometheus and Grafana monitoring. It hosts the internal chatbot (15,000 users), the coding models and the production vision models.
Turned MobileAI, our vision system in four plants, into a no-code platform where line engineers collect data, label and train models themselves. New models go live only after engineering review and are monitored for drift.
Lead technical direction for a team of 9. The team's work received 2 global and 2 local Ford innovation awards.

Software Engineer, Network & Systems

May 2019 – Dec 2022

Ford Otosan, Türkiye

Built Gözcü, a no-code platform for real-time accident detection on CCTV, deployable to a new camera in one click.
Wrote its inference backend in C/C++ (GStreamer, OpenCV, YOLO): 250+ concurrent RTSP streams, under 200 ms end-to-end latency.
Gözcü received Ford's President's Health & Safety Award in 2023.

Software Engineer

Jun 2018 – May 2019

Evstek Information Technologies, Istanbul

Developed the image-analysis software of KE-BOT, a robotic system for FUE hair transplantation (C++, OpenCV). The work was published at IEEE.

Open source

jcode, an agentic coding CLI in Rust

Aug 2026

3 bugs reported and root-caused, all fixed upstream

Agents on self-hosted models (vLLM, SGLang, Ollama) stalled after context compaction. Traced it to custom providers being routed through the OpenRouter runtime, which dropped orphaned tool outputs (#908).
Models without a declared input list were treated as image-capable, causing an unrecoverable HTTP 400 loop. Isolated it with an A/B matrix; the text-only default I proposed was adopted (#847).
Shifted symbols were lost on Turkish Q keyboards in the VS Code terminal. Identified the missing Kitty REPORT_ALTERNATE_KEYS flag (#870).

Ultralytics YOLO

May 2025

PR #20553, merged in release 8.3.131

Co-authored, with three others, the official YOLO11 example for Triton Inference Server in C++: gRPC client, FP16 preprocessing, NMS, CMake build.

Own projects

github.com/uzunenes

triton-server-hpa: scales Triton on Kubernetes by GPU utilisation (DCGM metrics, Prometheus adapter, HPA), with GPU time-slicing so it runs on one GPU.
k8s-ai-stack: self-hosted LLM stack on Kubernetes with vLLM, Ollama and OpenWebUI (OAuth2), plus throughput benchmarks.
puci: small AI coding-agent harness in C17; about 100 kB per session, runs 1,000 agents on one poll() loop for evaluation.
piciem, librtsplink, libmqttlink: image processing in plain C (DFT/FFT, convolution), RTSP and MQTT client libraries.

Skills

Inference: vLLM, SGLang, Triton Inference Server, TensorRT, OpenAI-compatible APIs
Platform: Kubernetes, OpenShift, Docker, ArgoCD, NVIDIA DCGM, Prometheus, Grafana, MLflow, GitHub Actions, Azure DevOps
Languages: Python, C, Bash (expert); C++, Go; Rust (reading and debugging)
ML and vision: PyTorch, YOLO, RT-DETR, OpenCV, GStreamer
Systems: Linux, strace, perf; TCP/UDP, gRPC, MQTT, RTSP
Spoken: Turkish (native), English (professional)

Publications and talks

Autoencoder-based video anomaly detection. 2nd International Conference of Engineering Sciences (ICES), Dec 2023.
Image analysis for the KE-BOT hair transplantation robot. IEEE Xplore, document 8719213.
Multi-site on-prem AI workloads on Kubernetes. Talk at Kubernetes Istanbul (CNCF community), Jul 2026.

Awards

Global Manufacturing Technical Excellence Award

Apr 2025

Ford Motor Company

For robotic vision in global manufacturing.

President's Health & Safety Award

Jun 2023

Ford Motor Company

For combining AI, computer vision and collaborative robots on the line. Signed by CEO Jim Farley.

Education

M.Sc. Electronics Engineering, coursework completed

2019 – 2023

Gebze Technical University

Research on unsupervised video anomaly detection with autoencoders (ICES 2023 paper above).

B.Sc. Electronics and Telecommunication Engineering

2013 – 2018

Kocaeli University

Thesis: real-time lane departure warning on an embedded platform.

Courses

Fast and Efficient LLM Inference with vLLM (Red Hat, 2026); Accelerate Your Model (Google DeepMind on DataCamp, 2026); Executive Leadership (Koç University, 2025); Certified Scrum Team Member (Scrum Inc., 2022).