MohammadReza
Mohammadzade
Seven-plus years turning research-grade AI into production systems that hold up under load — Agentic RAG, computer vision, and MLOps infrastructure built for enterprises that can't afford to get it wrong.
Core expertise
Four disciplines, one operating principle: AI that survives contact with production.
Enterprise Agentic AI & LLMs
Agentic RAG architectures, prompt engineering, and vector-native retrieval (QDrant, FAISS, Milvus, Neo4j) built for accuracy and reliability at enterprise scale — not demo scale.
Scalable Production Infrastructure
Standardized deployment through KServe, RayServe, vLLM, Triton, Docker, and MLFlow — the operational backbone that turns experiments into dependable systems.
Vision & Multimodal Intelligence
OCR, detection and tracking, semantic segmentation, super-resolution, and pose estimation — engineered to hold up in low-quality, real-world conditions.
AI Leadership at Scale
Building and mentoring high-performing AI teams, setting technical standards, and keeping engineering execution tied to business strategy.
Executive impact
Every figure below is tied to a shipped system, not a slide.
Journey & key milestones
From computer vision specialist to enterprise AI founder — six years of leading teams that shipped.
- Leading strategy and technical direction, building on a decade of hands-on production AI experience.
- Directed a 7-member cross-functional team delivering a production-grade AI decision-support platform.
- Accelerated executive strategic decision-making by up to 70%.
- Led an 8-person AI team, cutting model deployment time by 60% through standardized MLOps.
- Architected an Agentic RAG pipeline (LlamaIndex + Milvus) that cut hallucinations to 0.2%.
- Introduced AI-powered code-review and quality-scoring agents across the engineering organization.
- Built a face-recognition system with a 4% accuracy gain on under-represented demographics.
- Delivered an embedded license-plate recognition system for traffic analytics.
- Shipped an OCR pipeline reducing manual labeling effort by 65%.
- Orchestrated a visual programming platform for AI model composition, cutting prototyping time by 45%.
- Built time-series trajectory models, improving vehicle-movement prediction accuracy by 12%.
- Automated real-time ingestion pipelines, reducing manual effort by 90%.
- Developed deep networks for human-pose estimation and Persian-language semantic-similarity models.
- Foundational research work bridging academic rigor and applied AI.
Research & publications
A background in mechatronics and AI/robotics that shapes a systems-first approach to engineering.
Open to the right conversation.
Reach out for AI leadership discussions, strategic partnerships, advisory work, or investor introductions.