Ali Kazemi
Ali Kazemi

Senior AI/ML Engineer

Actively looking · Member since 23 Apr 2026
Message
Location
Iran
Desired salary
from 40 USD/hourly
Work preference
Full Time
Experience level
Not set

About

Professional summary

I am a Senior AI/ML Engineer with over 5 years of experience delivering production AI systems, including more than 2 years leading cross-functional teams of up to 15 members in client-facing environments. I specialize in building and scaling large language model (LLM) and retrieval-augmented generation (RAG) platforms, having successfully managed systems handling 2,000 requests per minute and over 20,000 daily messages. My work has significantly reduced onboarding costs by 96%, demonstrating my ability to deliver cost-efficient AI solutions.

My expertise includes developing high-performance NLP, OCR, and computer vision pipelines, achieving less than 50 ms OCR latency and 99.97% accuracy on biometric workloads involving 49 million images. I excel at translating complex business requirements into deployable AI solutions with measurable impact. I am fluent in English and open to relocating to Belgium to further my career.

Technically, I am proficient in Python and SQL, and skilled in machine learning frameworks such as PyTorch, TensorFlow, and Scikit-learn. I have extensive experience with transformers, LLM inference, RAG architectures, named entity recognition, OCR, and computer vision. I am also adept at data platforms including PostgreSQL, MongoDB, Elasticsearch, ChromaDB, and Milvus.

In terms of infrastructure and MLOps, I have architected Infrastructure as Code workflows using GitLab CI/CD and Ansible, automated provisioning of GPU clusters, and migrated monolithic systems to containerized microservices on AWS EC2 with SQS task queuing. I have implemented observability solutions using Prometheus, Grafana, and CloudWatch to monitor model drift and latency.

I have led the development of GenAI pipelines processing multimodal inputs to automate issue assignment, drastically reducing diagnosis times. Additionally, I have optimized open-source LLM deployments on NVIDIA RTX 5090 GPUs and engineered graph-based retrieval pipelines to handle massive knowledge bases with zero hallucination. My work on biometric AI systems includes fine-tuning face recognition models on large datasets and optimizing deep learning models for real-time edge deployment.

My freelance experience includes backend engineering for drone photogrammetry pipelines and developing automotive in-cabin AI systems with gesture and driver activity recognition. I am passionate about leveraging AI to solve complex real-world problems and continuously improving engineering productivity and system scalability.

Skills

12 capabilities

Experience

Career history

Product Tech Lead ParsTech AI

Led MLOps and infrastructure automation by architecting Infrastructure as Code workflows using GitLab CI/CD and Ansible to automate GPU cluster provisioning, reducing setup time from days to hours. Migrated monolith to containerized AWS EC2 microservices with SQS task queuing ensuring 99.9% uptime. Established monitoring with Prometheus, Grafana, and CloudWatch for model drift and latency. Built GenAI pipeline processing multimodal inputs to auto-assign Jira issues, reducing diagnosis time from 8 hours to 20 minutes. Scaled ParsChat support ecosystem to handle 20,000+ daily messages for e-commerce and customer support automation. Optimized deployment of QWEN models using vLLM on NVIDIA RTX 5090 infrastructure achieving 60 messages per minute throughput. Re-engineered retrieval pipeline into graph-based architecture with intent-based routing to process up to 10 million characters with zero hallucination. Reduced chatbot delivery time from 1 week to 5 minutes and cut setup costs from $2.50 to $0.10 per user. Fine-tuned face recognition models on 49M images using NVIDIA A6000 GPUs achieving 99.97% accuracy. Optimized deep learning models for CPU-based edge architecture achieving real-time processing. Deployed services using FastAPI, gRPC, Celery, and Redis for scalable task management.

Freelance Engineer Sensifai

As Backend Engineer for DIPAL (Sep 2025 – Mar 2026), engineered scalable AWS backend services automating structure-from-motion and multi-view-stereo pipelines for photogrammetry toolkit with REST API integration for GIS workflows. As ML/MLOps Engineer for SensiGesture (Feb 2022 – Jul 2022), curated proprietary in-cabin dataset with 350,000+ annotated RGB frames; trained MobileNetV3 models recognizing 22 gestures and 20 driver activity classes for real-time inference. Built end-to-end pipeline for data versioning, automated evaluation, and on-device deployment on automotive-grade edge hardware.

Education

Learning history

Khajeh Nasir University of Technology (KNTU)

M.Sc. Biomedical Engineering (Bioelectric)

GPA: 18.04 / 20 — Tehran, Iran

Ferdowsi University of Mashhad (FUM)

B.Sc. Electrical Engineering (Communications)

GPA: 15.8 / 20 — Mashhad, Iran

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