LLM/DL inference optimization • Deployment orchestration • Systems engineering
Agadir, Morocco · aymanebiri@gmail.com · +212 696 408 522

I build reliable, high-performance AI systems from first principles—bridging low-level engineering with pragmatic product delivery.
Currently at Omniops (KSA), focusing on LLM/DL inference performance (latency/throughput/cost) and deployment orchestration at scale.

• Serving pipelines for LLMs & DL models: token streaming, concurrency control, batching, KV cache/memory efficiency
• Orchestrating inference across clusters: Kubernetes + queues + autoscaling + observability
• Production toolchains: Python/TS, FastAPI/Flask, React, Docker, K8s, Postgres, Redis, RabbitMQ, Celery

• Led 1337 AI Exploration Lab (8 → 16 engineers): CV for industrial inspection, chemical process modeling, HR RAG chatbot, SFM stock tracking
• Built iOS Bluetooth plugin + proximity algorithm for Wiqaytna (Moroccan COVID tracing app)
• Security background (web audits, IR tooling) and microservices ERP (auth, BI, i18n, automation)
• Bronze — MCPC 2020, first to solve Problem C; 1st place OpenSourceDays 2019 & 2021

Languages: C/C++, Python, JS/TS
AI/Serving: vLLM, RAG patterns, (Ops: batching, streaming, caching, tracing)
Backend/Infra: FastAPI/Flask, Docker, Kubernetes, Celery, RabbitMQ, Redis, Postgres
Frontend: React
Domains: Inference perf, systems programming, reliability engineering

• Latency/Throughput/Cost trade-offs with measurable SLOs
• Determinism & debuggability via structured logs, traces, and health signals
• Simple-by-default architectures that scale without heroics

• caLLMe — Voice-first real-time LLM assistant (VAD → STT → Gen → TTS with interruptibility) — link
• K8s Inference Orchestrator — Queue-routed tasks, autoscaling, backpressure, observability
• HR RAG Chatbot — Policy/benefits QA with retrieval + structured outputs
• Industrial CV — Inspection + predictive maintenance pipelines

• Email: aymanebiri@gmail.com
• LinkedIn: link