Elnur
Shahbalayev
Senior AI & Computer Vision Engineer. I build perception systems that run at the edge — drones, robotics, industrial AI. Currently Team Lead at Bayraktar Teknoloji and AI Engineer at Automata Intelligence.
Engineer by craft,
researcher by instinct
I specialise in real-time computer vision for resource-constrained environments — Jetson-class hardware, drone platforms, industrial edge nodes. My work spans object detection, 3D stereo fusion, anomaly detection, and multi-stream video analytics.
Beyond CV, I'm actively expanding into LLM infrastructure and agentic AI — RAG pipelines, VLM fine-tuning, and production deployment patterns. I believe the next frontier is perception models that reason, not just detect.
I lead an AI perception team at Bayraktar Teknoloji (UAV systems), consult for Automata Intelligence on international government AI engagements, and teach AI/ML at ASOIU. Completing an MSc via a Warwick/ASOIU dual programme.
Where I've built things
- Lead perception pipeline for UAV systems: object detection, tracking, stereo depth, collision avoidance
- Deployed RF-DETR + TensorRT FP16 on Jetson Orin — 94% reduction in collision incidents in field trials
- Built multi-stream DeepStream pipeline processing 12M+ frames/day across concurrent drone feeds
- Achieved 47.2 mAP on VisDrone benchmark; integrated frequency-domain enhancement (FEFE module)
- Designed hierarchical classification loss for military/civilian vehicle distinction
- Lead AI engineering for government and enterprise clients across East Africa and the Gulf
- Delivered AI coordination layer for eGA (Zanzibar) — pivoted from citizen portal to intelligent API routing system after client discovery
- Built technical proposals and demo systems for ZRA, ZSSF, ZURA, ADNOC, and MB Group
- Designed AI Optimizer for ADNOC cogeneration/desalination plant in Ruwais; covered GTG/STG control, KPI methodology, and OT cybersecurity
- Teaching AI/ML Engineering to SABAH Groups (honours cohort) at national technical university
- Designed full-stack curriculum: Python prerequisites through production model deployment
- Created original Jupyter notebooks, assessments, and lecture materials from scratch
- Built real-time warehouse robotics tracking system using instance segmentation and Kalman filtering
- Developed anomaly detection pipeline for industrial inspection (PatchCore, custom augmentation)
- Satellite imagery segmentation for agricultural monitoring using U-Net variants
Things I've shipped
Full stack of tools
Computer Vision
- RF-DETR, YOLOv8/v11, RT-DETR
- Instance & semantic segmentation
- Stereo depth & 3D fusion
- Object tracking (ByteTrack, DeepSORT)
- Anomaly detection (PatchCore)
Edge Deployment
- TensorRT (FP16/INT8)
- ONNX Runtime
- NVIDIA DeepStream
- Jetson Orin / Nano
- GStreamer pipelines
LLM & Agentic AI
- RAG pipeline design
- LangChain / LlamaIndex
- VLM fine-tuning (LLaVA, Qwen-VL)
- Prompt engineering
- Agent orchestration
ML Engineering
- PyTorch (custom losses, training loops)
- Hugging Face ecosystem
- MLflow, Weights & Biases
- Docker, CI/CD
- FastAPI, Streamlit
Let's build
something
Open to senior/staff remote roles in computer vision, ML engineering, and LLM infrastructure. UTC+4, available for EU/UK hours.
elnur@shahbalayev.com